The Impacts of Macroeconomic Variables to Stock
Markets in Malaysia, Thailand, Indonesia and Philippines
Ooi Jun Shen
A research project submitted in partial fulfillment of the
requirement for the degree of
Master of Business Administration
Universiti Tunku Abdul Rahman
Faculty of Accountancy and Management
September 2015
The Impacts of Macroeconomic Variables to Stock
Markets in Malaysia, Thailand, Indonesia and Philippines
By
Ooi Jun Shen
This research project is supervised by:
Ng Ching Yat
Associate Professor
Department of Economics
Faculty of Accountancy and Management
Copyright @ 2015
ALL RIGHTS RESERVED. No part of this paper may be reproduced, stored in a
retrieval system, or transmitted in any form or by any means, graphic, electronic,
mechanical, photocopying, recording, scanning or otherwise, without the prior
consent of the author.
i
DECLARATION
I hereby declare that:
(1) This MKMA 25106 Research Project is the final result of my own work and that
due acknowledgement has been given in the references to all sources of
information be they printed, electronic, or personal.
(2) No portion of this research project has been submitted in support of any
application for any other degree or qualification of this or any other university, or
other institutes of learning.
(3) The word count of this research report is _ 27510__.
Name of Student
__Ooi
: Jun Shen ____
Student ID
__11UKM06208____
:
Signature
__________________
:
Date
__________________
:
ii
ACKNOWLEDGEMENT
First of all, I would like to express my appreciation to my research supervisor, Mr Ng
Ching Yat for the guidance, advice and patience to complete this research study.
I would like to also extend my appreciation to my parents, family and friends for their
support throughout this research study. Nonetheless, my sincere gratitude goes to all
of the respondents as well. Their cooperation in completing the questionnaires has
assisted me in obtaining data for research result analysis.
Overall, I would like to express my acknowledgement to all of the people who had
assisted and supported me in completing this research study. This study will not be
accomplished without the assistance from the above mentioned people.
iii
TABLE OF CONTENTS
Page
Copyright
i
DECLARATION
ii
ACKNOWLEDGEMENT
iii
TABLE OF CONTENTS
iv
CHAPTER 1 INTRODUCTION
1
1.0
Introduction
1
1.1 Research Background
2
1.2 Problem Statement
3
1.3 Research Objective
4
1.3.1 General Objective
4
1.3.2 Specific Objective
5
1.4 Research Questions
6
iv
1.5 Significance of the Study
6
1.6 Chapter Layout
7
1.7 Conclusion
8
CHAPTER 2 LITERATURE REVIEW
9
2.0 Introduction
9
2.1 Review of Stock Markets
9
2.1.1 FTSE Bursa Malaysia (KLSE)
10
2.1.2 The Stock Exchange of Thailand (SET)
12
2.1.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
14
2.1.4 The Philippine Stock Exchange (PSE)
15
2.2 Review of Theoretical Models
17
2.2.1 Stock Market Returns
17
2.2.1.1 Efficient Market Hypothesis Theory
17
2.2.1.2 Random Walk Theory
19
2.2.1.3 Modern Portfolio Model
20
v
2.3.1.4 Capital Asset Pricing Model (CAPM)
2.2.2 Consumer Price Index (CPI)
21
23
2.2.2.1 “Fed Model” of Equity Valuation
23
2.2.2.2 Fisher Effect Theory
24
2.2.3 Exchange Rate (ER)
24
2.2.3.1 The Scapegoat Theory
24
2.2.3.2 Flow-oriented Models
25
2.2.3.3 Stock-oriented Models
26
2.2.4 Gross Domestic Product (GDP)
27
2.2.4.1 Supply-Side Models
27
2.2.4.2 The Solow Growth Model
27
2.2.5 Interest Rate (IR)
29
2.2.5.1 ‘Substitution Effect’ Hypothesis
29
2.2.5.2 Taylor’s Theory
29
2.2.5.3 Arbitrage Pricing Theory (APT)
30
vi
2.2.6 Money Supply (M1)
31
2.2.6.1 Tobin’s Q Theory
31
2.2.6.2 Monetary Portfolio Model
32
2.3 Review of the Literature
33
2.3.1 Stock Market
33
2.3.2 Consumer Price Index (CPI)
35
2.3.3 Exchange Rate (ER)
38
2.3.4 Gross Domestic Product (GDP)
40
2.3.5 Interest Rate (IR)
42
2.3.6 Money Supply (M1)
45
2.4 Proposed Theoretical Framework
48
2.5 Conclusion
50
CHAPTER 3 METHODOLOGY
51
3.0 Introduction
51
3.1 Research Design
51
vii
3.2 Data Collection Method
52
3.2.1 Secondary Data
52
3.3 Sampling Design
54
3.3.1 Target Population
54
3.3.2 Sampling Element – Formula
54
3.3.2.1 FTSE Bursa Malaysia (KLSE)
54
3.3.2.2 The Stock Exchange of Thailand (SET)
56
3.3.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
57
3.3.2.4 The Philippine Stock Exchange (PSE)
59
3.3.3 Sampling Technique
62
3.3.4 Sampling Size
63
3.4 Data Processing
64
3.5 Multiple Regression Model
65
3.6 Hypotheses of the Study
66
viii
3.6.1 Consumer Price Index (CPI)
66
3.6.2 Exchange Rate (ER)
67
3.6.3 Gross Domestic Product (GDP)
67
3.6.4 Interest Rate (IR)
67
3.6.5 Money Supply (M1)
68
3.7 Data Analysis
68
3.7.1 Ordinary least square (OLS)
69
3.7.2 Unit Root Test
70
3.7.3 Johansen Cointegration
71
3.7.4 Granger Causality
72
3.7.5 Variance Decomposition
74
3.7.6 Impulse Response Function
75
3.8 Conclusion
76
CHAPTER 4 ANALYSIS AND FINDINGS
4.0 Introduction
77
77
ix
4.1 Descriptive Statistics
77
4.1.1 FTSE Bursa Malaysia (KLSE)
77
4.1.2 The Stock Exchange of Thailand (SET)
78
4.1.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
79
4.1.4 The Philippine Stock Exchange (PSE)
79
4.2 Ordinary Least Square (OLS)
80
4.2.1 FTSE Bursa Malaysia (KLSE)
80
4.2.2 The Stock Exchange of Thailand (SET)
82
4.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
83
4.2.4 The Philippine Stock Exchange (PSE)
84
4.3 Diagnostic Checking
85
4.3.1 Autocorrelation
85
4.3.1.1 FTSE Bursa Malaysia (KLSE)
86
4.3.1.2 The Stock Exchange of Thailand (SET)
86
x
4.3.1.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
87
4.3.1.4 The Philippine Stock Exchange (PSE)
4.3.2 Heteroscedasticity
87
88
4.3.2.1 FTSE Bursa Malaysia (KLSE)
89
4.3.2.2 The Stock Exchange of Thailand (SET)
89
4.3.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
90
4.3.2.4 The Philippine Stock Exchange (PSE)
4.3.3 Model Specification Test
90
91
4.3.3.1 FTSE Bursa Malaysia (KLSE)
92
4.3.3.2 The Stock Exchange of Thailand (SET)
92
4.3.3.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
93
4.3.3.4 The Philippine Stock Exchange (PSE)
4.3.4 Normality Test
93
94
xi
4.3.4.1 FTSE Bursa Malaysia (KLSE)
95
4.3.4.2 The Stock Exchange of Thailand (SET)
96
4.3.4.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
97
4.3.4.4 The Philippine Stock Exchange (PSE)
4.3.5 F-stats
98
99
4.4 Unit Root Test
100
4.4.1 FTSE Bursa Malaysia (KLSE)
101
4.4.2 The Stock Exchange of Thailand (SET)
102
4.4.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
103
4.4.4 The Philippine Stock Exchange (PSE)
104
4.5 Johansen Cointegration Test
106
4.5.1 FTSE Bursa Malaysia (KLSE)
106
4.5.2 The Stock Exchange of Thailand (SET)
107
4.5.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
108
xii
4.5.4 The Philippine Stock Exchange (PSE)
4.6 Granger Causality Test
109
109
4.6.1 FTSE Bursa Malaysia (KLSE)
110
4.6.2 The Stock Exchange of Thailand (SET)
115
4.6.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
119
4.6.4 The Philippine Stock Exchange (PSE)
124
4.7 Variance Decomposition
129
4.7.1 FTSE Bursa Malaysia (KLSE)
129
4.7.2 The Stock Exchange of Thailand (SET)
131
4.7.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
132
4.7.4 The Philippine Stock Exchange (PSE)
133
4.8 Impulse Response Function (IRF)
135
4.8.1 FTSE Bursa Malaysia (KLSE)
135
4.8.2 The Stock Exchange of Thailand (SET)
136
4.8.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
137
xiii
4.8.4 The Philippine Stock Exchange (PSE)
4.9 Conclusion
138
139
CHAPTER 5 CONCLUSION
140
5.0 Introduction
140
5.1 Summary of Statistical Analysis
140
5.1.1 Summary of Econometric Problems
140
5.1.2 Summary of Major Findings
142
5.1.2.1 FTSE Bursa Malaysia (KLSE)
142
5.1.2.2 The Stock Exchange of Thailand (SET)
143
5.1.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
145
5.1.2.4 The Philippine Stock Exchange (PSE)
5.1.3 Summary of Long-run Relationship
146
148
5.1.3.1 FTSE Bursa Malaysia (KLSE)
148
5.1.3.2 The Stock Exchange of Thailand (SET)
148
xiv
5.1.3.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
149
5.1.3.4 The Philippine Stock Exchange (PSE)
149
5.2 Discussion of Major Findings
150
5.3 Implications of the Study
154
5.4 Limitations of the Study
155
5.5 Recommendations for Future Research
157
5.6 Conclusion
158
REFERENCE
160
APPENDICES
179
xv
LIST OF TABLES
Page
55
Table 1:
Source of Data
Table 2:
Descriptive Statistic of Variables for Log(KLSE)
77
Table 3:
Descriptive Statistic of Variables for Log(SET)
78
Table 4:
Descriptive Statistic of Variables for Log(IDX)
79
Table 5:
Descriptive Statistic of Variables for Log(PSE)
80
Table 6:
Log(KLSE) is explained by Log(CPI). Log(ER),
Log(GDP), Log(IR) and Log(M1)
81
Table 7:
Log(SET) is explained by Log(CPI). Log(ER),
Log(GDP), Log(IR) and Log(M1)
82
Table 8:
Log(IDX) is explained by Log(CPI). Log(ER),
Log(GDP), Log(IR) and Log(M1)
83
Table 9:
Log(PSE) is explained by Log(CPI). Log(ER),
Log(GDP), Log(IR) and Log(M1)
84
Table 10:
Breusch-Godfrey Serial Correlation LM Test (KLSE)
86
Table 11:
Breusch-Godfrey Serial Correlation LM Test (SET)
86
Table 12:
Breusch-Godfrey Serial Correlation LM Test (IDX)
87
Table 13:
Breusch-Godfrey Serial Correlation LM Test (PSE)
87
Table 14:
Heteroskedasticity Test: Breusch-Pagan-Godfrey
(KLSE)
89
Table 15:
Heteroskedasticity Test: Breusch-Pagan-Godfrey
(SET)
89
Table 16:
Heteroskedasticity Test: Breusch-Pagan-Godfrey
(IDX)
90
xvi
Table 17:
Heteroskedasticity Test: Breusch-Pagan-Godfrey
(PSE)
90
Table 18:
RaPSEy RESET Test (KLSE)
92
Table 19:
RaPSEy RESET Test (SET)
92
Table 20:
RaPSEy RESET Test (IDX)
93
Table 21:
RaPSEy RESET Test (PSE)
93
Table 22:
Unit Root and Stationary Test Result (KLSE)
102
Table 23:
Unit Root and Stationary Test Result (SET)
103
Table 24:
Unit Root and Stationary Test Result (IDX)
104
Table 25:
Unit Root and Stationary Test Result (PSE)
105
Table 26:
Johansen-Juselius Cointegration Tests (KLSE)
107
Table 27:
Johansen-Juselius Cointegration Tests (SET)
107
Table 28:
Johansen-Juselius Cointegration Tests (IDX)
108
Table 29:
Johansen-Juselius Cointegration Tests (PSE)
109
Table 30:
Short- term Granger Causality Tests E-view Output
(KLSE)
110
Table 31:
Short- term Granger Causality Tests Result (KLSE)
111
Table 32:
Summary of Short-term Granger Causality Tests
Results between all variables (KLSE)
111
Table 33:
Short- term Granger Causality Tests E-view Output
(SET)
115
xvii
Table 34:
Short- term Granger Causality Tests Result (SET)
116
Table 35:
Summary of Short-term Granger Causality Tests
Results between all variables (SET)
116
Table 36:
Short- term Granger Causality Tests E-view Output
(IDX)
120
Table 37:
Short- term Granger Causality Tests Result (IDX)
120
Table 38:
Summary of Short-term Granger Causality Tests
Results between all variables (IDX)
121
Table 39:
Short- term Granger Causality Tests E-view Output
(PSE)
125
Table 40:
Short- term Granger Causality Tests Result (PSE)
125
Table 41:
Summary of Short-term Granger Causality Tests
Results between all variables (PSE)
125
Table 42:
Variance Decomposition of Log(KLSE) towards
Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1)
130
Table 43:
Variance Decomposition of Log(SET) towards
Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1)
131
Table 44:
Variance Decomposition of Log(IDX) towards
Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1)
132
Table 45:
Variance Decomposition of Log(PSE) towards
Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1)
134
Table 46:
Summary of Econometric Problems
141
Table 47:
Summary of Major Findings (KLSE)
142
Table 48:
Summary of Major Findings (SET)
144
Table 49:
Summary of Major Findings (IDX)
145
Table 50:
Summary of Major Findings (PSE)
146
xviii
Table 51:
Summary of Long-run Relationship (KLSE)
148
Table 52:
Summary of Long-run Relationship (SET)
148
Table 53:
Summary of Long-run Relationship (IDX)
149
Table 54:
Summary of Long-run Relationship (PSE)
149
Table 55:
Summary of Ordinary Least Square
150
Table 56:
Summary of Granger Causality Test
152
Table 57:
Summary of Johansen Cointegration Test
153
xix
LIST OF FIGURES
Figure 1:
Figure 2:
Framework of factors affecting stock returns in the Financial
Market of Malaysia, Thailand, Indonesia and The Philippine
from 2000-2014
Data Processing Diagram
Page
49
64
Figure 3:
Jarque-Bera Normality Test (KLSE)
95
Figure 4:
Jarque-Bera Normality Test (SET)
96
Figure 5:
Jarque-Bera Normality Test (IDX)
97
Figure 6:
Jarque-Bera Normality Test (PSE)
98
Figure 7:
Figure 8:
Figure 9:
The relationship between each variables for Granger
Causality Tests (KLSE)
The relationship between each variables for Granger
Causality Tests (SET)
The relationship between each variables for Granger
Causality Tests ((IDX)
Figure
10:
The relationship between each variables for Granger
Causality Tests (PSE)
Figure
11:
Impulse Response Function of Log(KLSE) to Shocks in
System Macroeconomic Variables
Figure
12:
Impulse Response Function of Log(SET) to Shocks in
System Macroeconomic Variables
Figure
13:
Impulse Response Function of Log(IDX) to Shocks in
System Macroeconomic Variables
Figure
14:
Impulse Response Function of Log(PSE) to Shocks in
System Macroeconomic Variables
xx
112
116
121
126
136
137
138
139
ABSTRACT
The relationship between stock market and macroeconomic variables is well
documented for the United States and other major economies. However, what is the
relationship between stock market and macroeconomic variables in emerging
economies? Stock market plays an essential role of an economy. From stock market,
one can easily predict the overall economy of the country. From various factors, stock
market is dependent and the impacts of these factors could be positive or negative.
Macroeconomic variables in economy that impact the stock exchange and
macroeconomic variables that affect the stock prices of any state are Government
policies, exchange rates, inflation, money supply, interest rate, unemployment rates,
foreign direct investment, law & order situation, political instability, national security,
Gross Domestic Product (GDP) growth rate, judiciary crises. For this purpose to find
out the impact of macroeconomic variables on stock market, four unlike variables are
supposed to study i.e. exchange rates, interest rates, unemployment rates, inflation
and GDP. This paper examines the relationships between the stock markets in
Malaysia, Thailand, Indonesia and The Philippines and five macroeconomic
variables, Consumer Price Index (CPI), Exchange Rate (ER), Gross Domestic
Product (GDP), Interest Rate (IR) and Money Supply (M1), from year 2000 to year
2014, which contains a monthly data pool. This paper applies Ordinary Least Square
(OLS) to examine the statistical relationship. Additionally, this paper investigates the
short run and long run dynamic linkages by using Johansen Co-integration Test and
Granger Causality test respectively. The result of this paper has achieved the main
objective of investigating the significant relationship between Consumer Price Index
(CPI), exchange rate (ER), Gross Domestic Product (GDP), interest rate (IR) and
money supply (M1) towards stock market returns of FTSE Bursa Malaysia (KLSE),
The Stock Exchange of Thailand (SET), Indonesia Stock Exchange (Bursa Efek
Indonesia, IDX) and The Philippine Stock Exchange (PSE).
xxi
CHAPTER 1
INTRODUCTION
1.0 Introduction
Nowadays, stock market returns is a broad and current interest in emerging nations
such as Malaysia, Thailand, Indonesia and others. These markets are playing an
important role in the global economy. Also, stock market returns is also act as an
indication of representing a nation’s economic activity as it shows how healthy is the
nation’s economy. The linkages between macroeconomic variables and stock market
returns are vital for relevant parties such as policy makers, investor and others. Thus,
this has attracted their concern from time to time (Kutty, 2010).
The impact of macroeconomic variables on stock prices is one of the crucial measures
to identify informational inefficiency of the stock market. Although the study and
investigation of relationship between macroeconomic variables and stock market
returns in emerging nations is a sought after topic, most of the researches have been
conducted on countries like Kenya, India, Brazil and others, not much studies were
conducted for emerging nations in Southeast Asia such as Malaysia, Thailand,
Indonesia and The Philippines. Thus, this research is aimed to further study on the
relationship between macroeconomic variables and the stock market returns for
emerging countries in Southeast Asia. Macroeconomic variables studied are
Consumer Price Index (CPI), Exchange Rate (ER), Gross Domestic Product (GDP),
interest rate (IR) and Money Supply (M1). The countries in this paper are Malaysia,
Thailand, Indonesia and The Philippines.
1
1.1 Research Background
Financial markets throughout the world these days have become significantly
cointegrated with each other. An effect in a particular nation’s financial market could
possibly bring minor or major impacts to the other nations.
Stock markets play an essential role in the global market that will affect the economy
and its importance has also been well recognized in the viewpoints of market players
or users. By collecting funds and capital in the stock market, listed companies or
firms are being benefited with long-term capital to develop or expand their
businesses. Apart from that, stock market provides more possible alternatives for
market players and users to make investment with their extra funds or savings.
However, these people have to observe the performance the companies and the stock
market carefully before making their decisions.
Recently, in the last two and a half decades, it is noticed that emerging countries’
stock markets are developing rapidly. There were several attempts to develop these
emerging economies. However, the conclusions were similar as most of these markets
are tend to be unstable. More time and efforts needed for these stock markets to be
developed (Engel and Rangel, 2005).
In addition, these stock markets are more likely to be responsive to factors like
political and international economic environment changes, as well as changes in the
macroeconomic activities. Therefore, investors tend to analyze these changes so that
they are able to examine the potential economic fundamentals of particular markets
and also to formulate expectations about it.
From the viewpoints of the professionals, academicians, as well as the investors, the
effect of macroeconomic variables on stock market returns is always a concern or
interests that need to be analyzed further. According to Efficient Market Hypothesis
(EMH), in an efficient market, it is believed that the movement of stock prices is
2
reflecting the information on macroeconomic factors.Therefore, it is believed that
unusual profits are not achievable in this kind of markets. If this is true, then any
changes in macroeconomic variables should not bring any impacts to stock market
returns. Nevertheless, this statement has been significantly examined by various
scholars who claimed that macroeconomic variables do affect stock prices and bring
impacts to stock market returns.
1.2 Problem Statement
Stock markets play a vital role in emerging countries in nurturing capital formation
and supporting economic growth. Stock markets are important for economic growth
as it ensures the movements of capital and funds or the investments to respective
sectors or industries in a country, which will eventually contribute to economic
growth. Therefore, macroeconomic variables are used to identify and examine the
relationship between stock market returns in Southeast Asian emerging countries.
In order to ensure the correct decisions are made all the time, it is important for the
market users or players to understand the correlations between macroeconomic
variables and stock market returns. As usual, research documents on relationships
between macroeconomic variables and stock market returns of developed countries
can be easily found, however literatures that focus on emerging economies are very
limited, especially Southeast Asian emerging countries.
Furthermore, due to limited literatures available in the public, market users are having
difficulties to identify the interdependencies of macroeconomic variables and stock
market returns. Generally, stock market returns are dependent on various
macroeconomic variables. However, different studies and researches have different
3
viewpoints on this topic. Therefore, more studies and research are necessary to
provide better insights for market users for the purpose of investments, decision
making and others, especially after the financial crisis in Asia and the implementation
of capital control in Southeast Asian’s emerging countries.
In short, this paperplans to further examine the relationships of the macroeconomic
variables with the stock market returns in the emerging countries in Southeast Asia
and contribute some useful information to economists, policy makers as well as the
potential investors to these countries.
1.3 Research Objective
1.3.1 General Objective
The main objective of this study is to investigate the effect of macroeconomic
variables in the stock market returns of Southeast Asian emerging countries
(Malaysia, Thailand, Indonesia and The Philippines). It can be a useful tool
and information for the stock market participants. With more understanding
on their relationships, it will also help to reduce the probability of future
losses in the stock market. The investigation of time-series relationship
between macroeconomic variables and stock price is included in this study.
4
1.3.2 Specific Objective
This paper seeks to:
i)
Examine the overall relationship between stock market returns of
FTSE Bursa Malaysia (KLSE) and each of the macroeconomic
variables, which are Consumer Price Index (CPI), Exchange Rate
(ER), Gross Domestic Product (GDP), and Interest Rate (IR),
Money Supply (M1).
ii)
Examine the overall linkages between stock market returns of The
Stock
Exchange
of
Thailand
(SET)
and
each
of
the
macroeconomic variables, which are Consumer Price Index (CPI),
Exchange Rate (ER), Gross Domestic Product (GDP), and Interest
Rate (IR), Money Supply (M1).
iii)
Examine the overall correlation between stock market returns of
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) and each
of the macroeconomic variables, which are Consumer Price Index
(CPI), Exchange Rate (ER), Gross Domestic Product (GDP), and
Interest Rate (IR), Money Supply (M1).
iv)
Examine the overall relationship between stock market returns of
The Philippine Stock Exchange (PSE) and each of the
macroeconomic variables, which are Consumer Price Index (CPI),
Exchange Rate (ER), Gross Domestic Product (GDP), and Interest
Rate (IR), Money Supply (M1).
v)
Identify relationships between stock prices and consumer price
index (CPI).
vi)
To analyze the relationship between Gross Domestic Products
(GDP) and its effects on stock market returns.
vii)
To investigate the correlation between stock prices and exchange
rates in the emerging countries in Southeast Asia.
5
viii)
Utilize interest rates as independent component to investigate the
correlation with stock market returns.
ix)
Obtain understanding on whether money supply affects stock
prices to fluctuate in the emerging countries in Southeast Asia.
x)
To examine the long run and short run relationship between
dependent and independent variables.
1.4 Research Questions
i) Can quarterly time-series data of stock market returns explain the relationship
by the corresponding macroeconomic variables of Consumer Price Index
(CPI), Exchange Rate (ER), Gross Domestic Product (GDP), interest rate (IR)
and money supply (M1) in Malaysia, Thailand, Indonesia and The
Philippines?
ii) Which selected macroeconomic variables have significant relationships with
stock market returns in long short run and short run?
iii) Would the determinants be helpful to the stock market participants in
forecasting movements of stock market returns?
1.5 Significance of the Study
The purpose of this paper is to examine the relationships between dependent variables
(stock market returns) and independent variables (macroeconomic variables such as
6
Consumer Price Index, exchange rate, Gross Domestic Product, interest rate and
money supply). This information on macroeconomic variables is important for market
users as it acts as a guideline to them in making their investment decision.
This study tends to utilize the information from previous researchers coupled with the
current study to extend more recent information to the stock market participants.
Apart from that, this study offers valuable contribution to current limited theoretical
and practical literature on stock market returns of Malaysia, Thailand, Indonesia and
The Philippines, by investigating the relationships between stock market return and
selected macroeconomic variables. This paper can assist investors in setting the basis
to make informed choices in regards to investment decisions, as it is crucial to the
establishment of public and private policies that moving towards improving the
stability and efficiency of stock markets.
This study will also help the relevant parties to understand that the same
macroeconomic variables will bring different relationship or effects to different
countries. This will trigger the academics, policy makers or investors to refer to
multiple sources of analysis instead solely rely on a few studies.
1.6 Chapter Layout
The subsequent chapters in this paper arearranged in the following manner. Chapter 2
will review previous literatures and theoretical framework will be presented as well.
Chapter 3 will introduce the methodology that will be used in this paper. In Chapter
4, results and findings will be presented and interpreted. Lastly, Chapter 5 will
include summary on findings, limitations and recommendation as well as the
7
conclusion for this paper.
1.7 Conclusion
Chapter 1 introduces the relationship between macroeconomic variables and stock
market returns. This chapter briefly explains the background and the operations of
stock markets, which will lead to a better understanding on the following chapters.
Also, the intention of investigating the significant correlation between the
macroeconomic variables (Consumer Price Index, Exchange Rate, Gross Domestic
Product, interest rate and money supply) and stock market returns is also presented.
8
CHAPTER 2
LITERATURE REVIEW
2.0 Introduction
A number of journals were reviewed regarding this topic. This study finds that
developing countries have become most of the researchers’ favorites but not only
target on developed countries instead. This paper will mainly focus on countries in
Southeast Asia where most of them are developing countries. The relationship
between macroeconomic variables and stock market returns in these countries will be
explained in this chapter.
2.1 Review of Stock Markets
Stock market is a platform where the shares of public listed firms are issued and
traded through exchanges or over-the-markets. Another term for stock market is
equity market; it is an important component of a free-market economy. Stock market
provides firms with the opportunities to collect funds or capital through this platform
and in exchange of having investors as the member of shareholders in the company.
Also, it gives opportunity for small investors to own a company without having the
risks of starting a new company with a large number of initial funds or capital.
9
Moreover, stock market allows the shareholders to share the financial achievements
their companies. Shareholders or investors will make profit from the dividend
distributed by the company if the company is making profit. In contrary, shareholders
and investors might lose their investments if the company is having loses, which
means that the company share price is going down and investors are forced to dispose
the shares with a lower price (Investopedia, 2015).
Stocks and shares are traded through exchange, which is a platform for investors to
trade their shares. A stock can only be traded, purchased or sold if it is listed on a
stock exchange platform. Therefore, this is a platform where both stock traders meet
each other (The Economic Times, 2015).
Numerous studies on how macroeconomic variables bring impacts to stock market
returns have been studied and examined by a numerous researches during last few
decades. For instance, Pilinkus (2009) analyzed the correlation between selected
macroeconomic variables and stock market of Lithuanian. Gan et el. (2006)
investigated the relationshipbetween stock market of New Zealand and a group of
selected macroeconomic factors. Harasheh and Abu-Libdeh (2011) haveselected
variables such as Gross Domestic Product (GDP), exchange rate, inflation rate and
others to review the stock price in Palestine. The conclusions of these studies find that
macroeconomic variables will influence the stock market returns.
Macroeconomic variables selected in this paper are Consumer Price Index (CPI),
exchange rate (ER), Gross Domestic Product (GDP), interest rate (IR) and money
supply (M1), which will be discussed further in this chapter.
2.1.1 FTSE Bursa Malaysia (KLSE)
10
The Stock Exchange of Malaysia was formed in year 1964. In 1965, with the
separation of Malaysia and Singapore, the Stock Exchange of Malaysia then
renamed asthe Stock Exchange of Malaysia and Singapore. In 1973, currency
interchangeability between Singapore and Malaysia stopped, and the Stock
Exchange of Malaysia and Singapore then split into the Stock Exchange
of Singapore andFTSE Bursa Malaysia Berhad (“FTSE Bursa Malaysia
KLCI”, 2011).
On April 14 2004, following the demutualization exercise, FTSE Bursa
Malaysia then renamed as FTSE Bursa Malaysia Berhad. The objective of the
changes is to strengthen the competitive position in the global market. It
comprises a Main Board, a Second Board and Malaysian Exchange of
Securities Dealing and Automated Quotation (MESDAQ). At that point of
time, the total market capitalization ofFTSE Bursa Malaysia is MYR700
billion (US$189 billion) (“FTSE Bursa Malaysia KLCI”, 2011).
FTSE Bursa Malaysia targets on numerous initiatives, the purpose of which is
to improve its service and product offerings, increase the efficiency of its
business, increasemarket’s velocity and liquidity and achieve economies of
scale in its operations (“FTSE Bursa Malaysia KLCI”, 2011).
Kuala Lumpur Composite Index (KLCI) is the main index and market
indicator in
Malaysia.
It
provides
market
participants
thedirection
performance on the stock market returns of Malaysia as well as the health
condition of the stock market. KLCI consists of 100 largest companies from
main board that valued by full market capitalization from Bursa Malaysia and
comprises multi-sectors companies (“FTSE Bursa Malaysia KLCI”, 2011).
There are two main requirements that stated in FTSE Bursa Malaysia Ground
Rules that need to be fulfilled by the companies that listed in KLCI. The two
requirements are liquidity and free float. For liquidity, it is to ensure that the
firms’ stocks are enough to be traded by the market participants. Also, a
minimal of free float is 15% for each firm and the main objective for this is to
11
determine the attribution of firms’ market activity in KLCI (“FTSE Bursa
Malaysia KLCI”, 2011).
In order to provide a tradable, investable and transparently managed index,
FTSE Bursa Malaysia KLCI implements index calculation methodology that
is internationally accepted by the global market. It is calculated by FTSE that
applies the closing prices and real time thatderived from Bursa Malaysia.
Calculation is based on a value weighted formula and adjusted by a free float
factor. Also, it is calculated in every 15 minutes (“FTSE Bursa Malaysia
KLCI”, 2011).
2.1.2 The Stock Exchange of Thailand (SET)
Introduction The modern Thai Capital Market traces its origins back to the
early 1960s. In 1961 Thailand implemented its first five-year National
Economic and Social Development Plan to support the promotion of
economic growth and stability as well as to develop the Kingdom's standard
of living. Following upon this, the Second National Economic and Social
Development Plan (1967-1971) then proposed for the first time that an orderly
securities market be established in order to mobilize additional capital for
national economic development (“History and Roles”, 2015).
The creation of Thailand's first officially sanctioned and regulated securities
market was initially proposed as part of the Second National Economic and
Social Development Plan (1967-1971). In outlining its proposal for the
creation of a supervised securities market, the Second National Development
12
Plan stressed that the market's most important role would be to mobilize funds
to support Thailand's industrialization and economic development (“History
and Roles”, 2015).
The modern Thai equity market is separated into two stages, beginning with
"The Bangkok Stock Exchange" that isowned privately, followed by the
formation of "The Securities Exchange of Thailand" (“History and Roles”,
2015).
In July 1962, Thai stock market beginswhen a private group formed an
organized stock exchange as a limited partnership. Then, it became a limited
company and renamed to the "Bangkok Stock Exchange Co., Ltd." (BSE) in
1963 (“History and Roles”, 2015).
In 1968, the annual turnover value only consists of 160 million Thai bath and
it was considered as an inactive stock market. Not only that, the trading
volumes continued to reduce later to 114 million 46 million Thai bath in year
1969 and 1970 respectively. Therefore, it wasconcluded that BSE failed to
succeed because of insufficient support from the Thai government as well as
limited understanding of the stock market by the market participants (“History
and Roles”, 2015).
In spite of the failure of the BSE, the concept of securities market in Thailand
had by then attracted substantial attention. In this regard, the Social
Development Plan and Second National Economic proposedto form and
develop similar market with suitable facilities as well as following similar
procedures for securities trading (“History and Roles”, 2015).
In 1969, World Bank recommended for the government to acquire the services
of Professor Sidney M. Robbins from Columbia At the same time, the Bank of
Thailand also established a Working Group on Capital Market Development
with the objective to form the Thai stock market. In 1970, Professor Robbins
introduced a report entitled "A Capital Market in Thailand". This report then
became the master plan for the future development of the Thai capital market
13
(“History and Roles”, 2015).
In 1972, Thai Government amended the "Announcement of the Executive
Council No. 58 on the Control of Commercial Undertakings Affecting Public
Safety and Welfare". In May 1974, with the amendments, "The Securities
Exchange of Thailand" (SET) then was endorsed. By 1975 the fundamental
legislative framework was in place and "The Securities Exchange of Thailand
then officially formed and started trading on the April 30, 1975. In 1991, it
renamed to "The Stock Exchange of Thailand" (“History and Roles”, 2015).
2.1.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Different from other index, all Listed Companies are the criteria of
Jakarta Composite Index (JCI) in order to construct the index
calculation. Indonesia Stock Exchange is the only responsible party to
reduce one or more Listed Companies from the calculation. This is to
ensure JCI will shows the real and fair market condition. In order for
the reduction to take place, there is a condition to be fulfilled. If Listed
Company’s public shares only owned by a few shareholders (small
free float) and at the same time, the market capitalization is high, the
reduction may take place. Due to price change of Listed Company’s
stock, there might be an impact on normal fluctuation of the JCI (“The
Capital Market”, 2010).
JCI is the index that owned by Indonesia Stock Exchange. However,
Indonesia Stock Exchange is not liable for the products that offered by
14
users who use JCI as their benchmark. Besides, Indonesia Stock
Exchange is not accountable for any investment decisions made by any
Parties that use JCI as a benchmark (“The Capital Market”, 2010).
The LQ45 Index, which was launched in February 1997, is a market
capitalization-weighted index that captures the performance of 45 most
liquid companies listed on the Indonesia Stock Exchange (the “IDX”).
The LQ45 Index covers at least 70% of the stock market capitalization
and transaction values in the Indonesia Stock Market. The Index is
denominated in Indonesia rupiah (“IDR”) and is published throughout
the trading hours of the IDX (“The Capital Market”, 2010).
2.1.4 The Philippine Stock Exchange (PSE)
The Philippine stock market is one of the earliest stock exchanges established
in Asia and it has a rich history of events that have contributed to its
development. It is also considered as a barometer of future economic
performance and for years has served its primary functions of facilitating the
dual role of capital rising for companies and trading of shares by investors
(“The Philippines Stock Exchange”, 2012).
The first stock exchange in the Philippine was set up on 08 August 1927
during the American colonial period as the Manila Stock Exchange, Inc.
(MSE). Operations ceased during the Japanese occupation and resumed in
1946 after Japan’s surrender in 1945. On 27 May 1963, the Makati Stock
Exchange, Inc. (MKSE) was organized. The MKSE started operations on 16
November 1965. Eighteen companies were listed in the MKSE on its first day
15
of operations. The Philippines Stock Exchange, Inc. (PSE) was established on
14 July 1992, in anticipation of the unification of MSE and MKSE. Composite
indices were introduced in MKSE and MSE in 1978 and 1986, respectively, in
order to measure market movement. The one price-one market exchange was
achieved through the link-up of the two existing trading platforms on 25
March 1994 (“The Philippines Stock Exchange”, 2012).
In 2006, to accommodate the growing diversity of listed companies in the
Exchange and provide better sector comparable, the industry classification of
listed companies was revised and companies were classified according to their
major source of revenue, instead of the primary purpose stated in their articles
of incorporation. The six sectors currently being used were established,
namely, Financials, Industrials, Holding Firms, Property, Services, and
Mining & Oil (“The Philippines Stock Exchange”, 2012).
Whole day trading was implemented on the first trading day of 2012, starting
at 9:30am, with a recess at 12pm to 1:30pm, and closing at 3:30pm. This
aimed to align the PSE’s trading hours with other Asian stock exchanges as
well as to increase market liquidity by opening up trading in the PSE to
markets in other time zones (“The Philippines Stock Exchange”, 2012).
16
2.2 Review of Theoretical Models
2.2.1 Stock Market Returns
2.2.1.1 Efficient Market Hypothesis Theory
Efficient Market Hypothesis (EMH) theory is part of an important
aspect in today’s of moderneconomy. The concept of EMH is easy to
understand, it is a model about how markets will perform and it is said
be an efficient market if the stock prices in market reflect the
information available in the market.
Generally, the fundamental information about firms or companies will
be reflected in their stock prices in an efficient stock market.In another
words, a particular market is efficient if the prices successfully reflect
the available information that are circulating in the market. This is also
supported by Eakins and Mishkin (2012), where they claimed that
asset prices will be fully reflected with available information in an
efficient market.
However, it is possible that the price movement is not completely
reflect according to the available information in the market and thi
could be due to the dissimilarity in market players’ awareness as well
as the abnormal transactions made in the market. (Goedhart, Koller
17
and Wessels, 2010).
According to Allen, Brealey and Myers (2011), they claimed thata
market is deemed to be efficient when the market is impossible to have
a return that is higher than the market.
Market efficiency can be categorized into three stages, which are c,
semi-strongform of market efficiency and strong forms of market
efficiency,with the conditions of all available information is reflected
in the stock price.
In weak form of market efficiency, stock prices reflect thefundamental
information that relates to the historical stock price movements.There
are lower possibilities for investors to make abnormal profit or return
in the market as all the historical information is available and
circulating in the market. Hence, surplusprofit might not be available if
the market is in the status of weakly-efficient.
Semi-strongly efficient stock market prices reflect the fundamental
information about historical stock prices as well as the current
available information that is circulating in public. Current information
could be proposal of merger and acquisitions, announcements of
dividend pay-outs and others.
Strongly efficient market will reflect all possible informationregardless
they are circulating in public or not. Strongly efficient market implies
thatmispriced stocks are not feasible and it is not possible to have the
opportunities to earn excess returnbecause trading on insider
information
has
no
contribution
anymore
(Malkiel,
2011).
However,some researchers did claim that it is still possible to have
strongly efficient market as insider trading is not legal in the market
(Schwert, 2003).
In an efficient market, apart from reflecting the insider and public
18
information on the stock prices, it is also related to other assumptions
and financial models. Firstly, market efficiency will also be affected
by the rationality of market players or investors. In fact,not all the
trading is based on rational analysis but just an assumption made buy
the investors.Nevertheless,there is argument claimed that this should
not bring impacts to the stock prices as the probability of random
trading is interrelated (Shleifer, 2000).
According to Goedhart et el., (2010), theystated that investors can be
categorized into 3 group, which are traders, intrinsic value
investorsand mechanical investors. The dissimilarity among them is
the concept or basis of their investment or trading decision. Traders are
using technical analysis, intrinsic value investors are using
fundamental analysis and mechanical traders perform trades according
to rules.
2.2.1.2 Random Walk Theory
The Random Walk Theory finds its origin in the early works of
Bachelier back in 1900. Extended and translated into English by
Cootner in 1964 this theory submits that stocks at the end of a certain
time period largely show future prices. These seem to be generated by
a random process and show independent (Gaussian or normal
standard) distributions. Other chartist theories however share the
common assumption that history repeats itself and therefore historical
stock price behaviours can be used to predict a share’s price.
In 1990, Bachelier inductively transferred botanic observations like the
19
Brownian motion to build a mathematical model to explain price
fluctuations on the stock market. Even though both tried to justify this
theory empirically, they felt short as they only used cross-sectional
data. In 1962, Moore analyzed eight shares from the U.S. Stock market
(NYSE). They observed an approximately normal distribution;
however they acknowledged that most of the distributions were
leptokurtic which weakens their findings. To provide more reliable
facts, Fama et al, (1965) analyzed the whole Dow-Jones Industrial
Average index (30 stocks).
The efficiency of information also plays a major role within this
research area. If any information is distributed or accessible to/from
each investor there would not be any fluctuation or variation in stock
prices. Only when new information is created the market reacts (Fama
et al, 1965). If the market (buyers and sellers) knows about a
company`s future, this would already be reflected in the current stock
price. As information is processed in different ways and there is
existing disagreement about a company’s intrinsic value stock prices
fluctuate randomly. Fama et al, (1965) calls it the market’s “noise” and
forms a fundament for short-term behavioral models like the one of
Barberis. According to Fama et al, (1965) this does not contradict the
long-term market efficiency but underlines its power. One of the best
established investment strategies, the long-term focused buy and hold
approach, is based on this idea.
2.2.1.3 Modern Portfolio Model
20
In 1952, Harry Markowitz developed Modern portfolio model (MPT)
(Fabozzi, Gupta and Markowitz, 2002). Markowitz claims that the
largest challenge for an investor is to discover the perfect combination
of risky assets,stocks, in regards to expected return and variance of
return.
A basic concept for perfect combination of stocks will be a portfolio
that will generate highest return will not be generated with the
portfolio with the lowest risk (variance). This concept assumes that
greater expected return of a portfolio happens when investorsare likely
to beargreater risk. In contrary, risk-averse investorswill be able to
minimize the variance in exchange to a lower expected return.
Generally, MPT assumes that if investors are risk averse, they will
only focus ononce off investment return when they are doing
portfolios selection (Fama and French, 2003). Fama et al (2003) has
confirmedthatholding constant expected return will minimize variance
and holding constant variance will maximize expected return. Market
participants can simplyformtheirfavored portfolio based on the
formulation of an efficient frontier, depending on their risk appetites.
2.3.1.4 Capital Asset Pricing Model (CAPM)
Capital
Asset
Pricing
Model
(CAPM)
is
developed
after
HarryMarkowitz’s Modern Portfolio model (Fama et al, 2003). It
supposes that investmentopportunity set is ageneral knowledge- prices
reflection to the fresh informationso as to fall along the new trading
21
prices (Kumar et al., 2006). However, this model is not supported by
several market professionals in security markets.
CAPM adopted the assets pricing theory ofJohn Linther and William
Sharpe (Fama et al, 2003). It is attractedby its pleasing predictions and
simple logic about how risk measurement or assessment is done on the
linkages between the risk and expected return.
Generally, the idea and concept behind this model is where market
participants to be compensated in two approaches, which are time
value of risk and money. Risk free rate (rf) is representing the time
value of money and compensates the investors for their investments
over a period of time. Additionally, time value for risk is representing
the risk and calculates the amount of money that investors need to
contribute for taking extra risks. This is calculated by taking a risk
measure (beta) that compares the returns of the asset to the market for
a certain period of time as well as to the market premium (Rm-rf).
22
2.2.2 Consumer Price Index (CPI)
2.2.2.1 “Fed Model” of Equity Valuation
The
impacts
of
the
macroeconomic
on
the
stock
market
returnscombine the different ideas and efforts from academics,
investment professionals, and monetary policymakers. Different
practitioners have various contributions to the stock market. The
leading practitioner model of equity valuation, which is known as “Fed
model”, relates the yield on stocks (as measured by the ratio of
dividends or earnings to stock prices) to the yield on nominal Treasury
bonds (Campbell and Vuolteenaho, 2004).
The main idea behind this theory is that the stocks and bonds compete
for space in investors’ portfolio. If the yield on bond increases, the
yield on stocks will also have an upward trend in order to maintain the
competitiveness of stocks.
According to Campbell et al (2004), if the measured stock yield
exceeds the normal yield defined by the Fed model, then stocks are
attractively priced which is underpriced. However, if the measured
yield falls below the normal yield, then stocks are overpriced. Inflation
would be the main catalyst in affecting the nominal bonds yields. In
short, Fed model concludes that stock yields are highly correlated with
inflation. However, in the late 1990’s, practitioners often argued that
23
falling stock yields, and rising stock prices, were justified by declining
inflation (Campbell et al, 2004).
2.2.2.2 Fisher Effect Theory
Fisher effect theory describes the long run relationship between
inflation and interest rate (‘Fisher Effect’, 2011). It stated that inflation
and interest rate are moving parallel with same amount or percentage.
In this case, government plays an important role when they want to
implement policy instrument. People do care about how government
control on money supply as it will definitely bring impactsto stock
market returns. From Foote (2010), when government control on
money supply, it would help to determine the inflation in long run at
the same time it will move the nominal interest rate. Finally, the bond
prices will be affected as well as demand for the stocks because bond
price moves with an inverse relationship with interest rate.
2.2.3 Exchange Rate (ER)
2.2.3.1 The Scapegoat Theory
24
The essence of the scapegoat theory of exchange rates is that at times
some macroeconomic factors receive an unusually large weight and
thus are made scapegoats of exchange rate movements. This scapegoat
effect arises because of agents’ “rational confusion” as they make
inference on the true parameters of the model only conditioning on
observable fundamentals and exchange rate movements at times when
the exchange rate is instead driven by unobservable (e.g. large order
flows). Thus, when exchange rates move strongly in response to
unobservable, it is rational for agents to blame factors that they can
actually observe, and more precisely those macro fundamentals that
are out of sync from their longer term equilibrium values and move
consistently with observed exchange rates. This scapegoat effect can
generate an unstable relationship between exchange rates and macro
fundamentals, driven mainly by the expectation of the structural
parameters and not by the structural parameters themselves (Marcel,
Dagfinn, Lucio and Gabriele, 2014).
2.2.3.2 Flow-oriented Models
Flow-oriented models assume that the exchange rate is determined
largely by a country’s current account or trade balance performance.
These models posit that changes in exchange rates affect international
competitiveness and trade balance, thereby influencing real economic
variables such as real income and output. Stock prices, usually defined
as a present value of future cash flows of companies, should adjust to
the economic perspectives. Thus, flow oriented models represent a
positive relationship between stock prices and exchanges rates with
25
direction of causation running from exchange rates to stock prices.1
The conclusion of a positive relationship stems from the assumption of
using direct exchange rate quotation (Stavarek, 2004).
2.2.3.3 Stock-oriented Models
Stock oriented models put much stress on the role of the financial
(formerly capital) account in the exchange rates determination. These
models can be distinguished on portfolio balance models and monetary
models. Portfolio balance models postulate a negative relationship
between stock prices and exchange rates and come to the conclusion
that stock prices have an impact on exchange rates. Such models
presume an internationally diversified portfolios and the role of
exchange rates to balance the demand for and the supply of domestic
as well as foreign assets. A rise in domestic stocks prices leads to the
appreciation of domestic currency through direct and indirect
channels. A rise in prices encourages investors to buy more domestic
assets simultaneously selling foreign assets to obtain domestic
currency indispensable for buying new domestic stocks. The described
shifts in demand and supply of currencies cause domestic currency
appreciation. The indirect channel grounds in the following causality
chain. An increase in domestic assets prices results in growth of
wealth, which leads investors to increase their demand for money,
which in turn raises domestic interest rates. Higher interest rates attract
foreign capital and initiate an increase in foreign demand for domestic
currency and its subsequent appreciation. According to the monetary
approach an exchange rate is the price of an asset (one unit of foreign
26
currency) and therefore the actual exchange rate has to be determined
by the expected future exchange rate similarly like prices of other
assets. The only factors influencing the actual exchange rate are those
which affect the future value of the exchange rate. Since developments
of stock prices and exchange rates may be driven by different factors
the asset market approach emphasizes no linkage between stock prices
and exchange rates (Stavarek, 2004).
2.2.4 Gross Domestic Product (GDP)
2.2.4.1 Supply-Side Models
Supply-side models assume that GDP growth of the underlying
economy flows to shareholders in three steps. First, it transforms into
corporate profit growth; second, the aggregate earnings growth
translates into earnings per share (EPS) growth, and finally EPS
growth translates into stock price increases (MSCI, 2010).
2.2.4.2 The Solow Growth Model
27
In the case of the Solow growth model, the key variable is labor
productivity: output per worker, how much the average worker in the
economy is able to produce. We calculate output per worker by simply
taking the economy’s level of real GDP, and dividing it by the
economy’s labor force. This quantity, output per worker, is the best
simple proxy for the standard of living and level of prosperity of the
economy. In every economic model, the Solow growth model has no
exception, economists analyze the model by looking for equilibrium: a
point of balance, a condition of rest, a state of the system toward
which the model will converge over time. Economists look for
equilibrium for a simple reason: either an economy is at its equilibrium
position, or it is moving to an equilibrium position. Once the
equilibrium position toward which the economy tends to move is
found, it can be used to understand how the model will behave. If the
right model is built, it will show how the economy will behave. In
economic growth, the equilibrium economists look for is an
equilibrium in which economy’s capital stock per worker, its level of
real GDP per worker, and its efficiency of labor are all three growing
at the exact same proportional rate. The equilibrium economists look
for in the case of the Solow growth model is balanced-growth
equilibrium. In this growth equilibrium the capital intensity of the
economy remains constant as the rest of the variables in the economy
grow. The amount of capital that the economy uses to produce each
unit of output remains constant over time, as both the capital stock and
output grow at the same proportional rate, and thus capital intensity
does not change (The Theory of Economic Growth, 2005).
28
2.2.5 Interest Rate (IR)
2.2.5.1 ‘Substitution Effect’ Hypothesis
Commonly, macroeconomic variables will influence stock market
returns. However, stock market can be affected by the changes in the
direction of monetary policy as well. From the restrictive policies, it
will make the cash flow worth less with higher rate of interest or
discount rates. Therefore, the attractiveness of the investment would
be reduced which in turn decrease the value of stock market returns.
From the ‘substitution effect’ hypothesis, a raise in interest rate would
increase the opportunity cost of holding cash, which will leads to a
substitution effect between stocks and other interest bearing securities
like bonds.
In summary, both the restrictive policy and the substitution effect
hypothesis suggest that interest rate should be inversely related to
stock market return (Rahman, Sidek and Tafri, 2009).
2.2.5.2 Taylor’s Theory
29
Macroeconomists are interested in modeling the Federal Reserve’s
“reaction function”. Federal Reserve’s “reaction function” shows how
Fed alters monetary policy in response to economic developments and
provides a basis in forecasting the short-term interest rate (Judd and
Rudebusch, 1998).
Taylor’s rule is a simple model by determining how central bank
should react to the changes of inflation, macroeconomic condition and
output level by changing the nominal interest rate. In order to
determine the central bank’s operating target for a short-term nominal
interest rate, both positive and normative accounts of monetary policy
are usually expressed in terms of systematic rules (Giannoni and
Woodford, 2002). Taylor’s rule expresses the Fed’s operating target
for the federal funds rate as a linear function of a current inflation rate
and a current measure of output relative to potential stock level
(Taylor, 1993).
2.2.5.3 Arbitrage Pricing Theory (APT)
Arbitrage pricing theory is an extension of Capital Asset Pricing
Model (CAPM). This is due to the several drawbacks of CAPM such
as having difficulty to measure true market portfolio. According to
Iqbal and Haider (2005), they proposed that there are several sources
of risk such as inflation and changes in aggregate output in the
economy that cannot be eliminated through diversification. APT
calculates a portfolio beta by estimating the sensitivity of an asset’s
return. With the increasing of the interest rate risk, it will lower the
asset’s return. Martikainen, Yli-Olli, and Gunasekaran (1991) used
30
interest rate as one of the variables in testing the APT model. He
explained that the higher the interest rate, the higher the discount
factor, and lower the stock prices.
2.2.6 Money Supply (M1)
2.2.6.1 Tobin’s Q Theory
Tobin’s Q theory tries to relate the monetary policy (money supply)
and share prices. According to Gonda (2003), economists expect
monetary policy might have an effect upon investment expenditure via
share prices.
From the theory, there is a confirmation of the existence mutual link
between Coefficients of q and investment expenditure (Gonda, 2003).
From his research, James Tobin defined q as the share of the market
value of an enterprise (the sum of share prices) and the replacement
cost of capital. He stated that when people have money supply in term
of money, people will tend to increase their spending (Gonda, 2003).
The demand for the security increase when people use their money to
invest in stock which will increase the stock prices.
The rising of the share prices increase a firm’s market value and thus
lead to a growth in the coefficient q and a growth in investment
expenditures.
31
The mechanism is as followed:
M↑ SP↑ q↑ I↑ Y↑
2.2.6.2 Monetary Portfolio Model
The monetary portfolio model is developed by Brunnerand Friedman
in 1961. They discovered in their analysis saying that they view money
as an asset among other assets in investor’s portfolios. Investors will
try to reestablish their desired money holdings by substituting between
money and other assets if there is a monetary supply shocks (Sellin,
2001). A monetary supply shock referring to a permanent increase in
nominal stock of M1 would generates a temporary drop in the interest
rate that consistent with the liquidity effect, a temporary increase in
real output and a permanent depreciation of the nominal exchange rate
(Kasumovich, 1996).
According to Brunner, Friedman and other researchers, investors will
typically respond with a lag, which would imply that money could
help to predict stock market returns. Friedman’s hypothesis which
derived by Sellin (2001), claimed that the real quantity of money
demanded relative to income is positively related to the real and
nominal equity price, but that the contemporaneous correlation is
negative. He offers three explanations of an inverse relationship
between price of equity and velocity with wealth effect, a risk
spreading effect and transaction effect (Sellin, 2001).
The first explanation starts with rising in prices of equity lead to an
32
increase in nominal wealth which in turn raises the higher wealth to
income ratio. The second rationale begins with higher equity prices
and higher expected excess returns on equity could reflect higher risk.
The last explanation from Sellin (2001) is that higher equity prices
would imply a higher dollar volume of transactions, which would
require increase money balances. All these offsetting effects are
substitution effect which is contemporaneous, since it purely involves
a rebalancing of investors’ portfolios and thus explains the negative
contemporaneous correlation between money and equity prices (Sellin,
2001).
2.3 Review of the Literature
2.3.1 Stock Market
Nowadays in an efficient stock market, whenever there is updated information
circulated in the market, the stock prices will immediately reflect to them,
sometimes the stock prices even adjusted before the information flow out to
the public. In order to estimate the movements of stock prices and make
profits, it is believed that there are difficulties in making investment decision
if market participants only rely on the readily available information in the
market. In short, efficient market responses toall information that circulating
in the market rapidly and stock prices will be reflected immediately. Apart
from that, stock prices also reflect the projectionsof future performances in the
market. In conclusion, if stock prices reflect the aboveinformation, then this
should be utilized as thedeterminants of economic activities. Therefore, it is
believed that the dynamic relationship between stock market returns and
33
macroeconomic variables can be guidance in making nation’s macroeconomic
policies (Maysami et al., 2004).
The recent growth in emerging countries’ stock markets is generating the
attention from both practitioners and academics. Spontaneously, researchers
tend to investigate and understand the nature of Southeast Asian countries by
delving the stock prices of these countries. The reason being is that stock
markets play a vital role in determining the future course of events in these
countries. Therefore, a number of literatures were conducted to examine the
linkages between macroeconomic variables and stock markets. Different
macroeconomic variables may give different effects to the stock markets,
which will influence the investors’ decisions in their investments. Seemingly,
this may become one of the motivations for researchers to examine the
relationship between macroeconomic variables and stock market returns.
Previously, studies and researches have been conducted to examine the
relationship between stock markets movement and macroeconomic variables.
However, different researchers came out with different findings. Some say
that there is relationship between macroeconomic variables and stock market
returns but there are also studies find that macroeconomic variables and stock
market returns are not correlated.Hence, previous studies will be reviewed and
discussed further to understand more.
There are also some other researchers show that it is not possible to explain
the movements of stock price by fundamental factors or vice versa. Shiller
(2000) claimed that stock prices movements could lead by speculative bubbles
or the irrational investors’ behavior.On top of that, studies done by Harvey
(2000) and Verma and Ozunab (2005) also find that macroeconomic variables
will not be able to explain the stock market returns in both developed and
developing markets.
34
2.3.2 Consumer Price Index (CPI)
Consumer Price Index (CPI) is a principal measure of price movements at
retail level. CPIshows the purchasing cost of goods and services that
consumed by private households (Subhani, Gul and Amber, 2010).
There are numerous studies conducted to examine the relationship between
stock market returns and Consumer Price Index (CPI). Findings done by Hu
and Willett(2000), Cauchie, Hoesliand Isakov, (2003) Ahmed and Mustafa,
(2012), show evidence on the presence of negative relationship between CPI
and stock market returnsfor various countries.
According to Heng, Sim, Tee and Wong (2012), CPI is the proxy of inflation
and deflation as CPI is one of the most frequently used statistics to identify the
periods of inflation and deflation. In another words, inflation rate will be
reflected by CPI represents an overall upward price movement of goods and
services.
However, from the prediction of Fisher effect, it shows that stock market
returns should have positive relationship with expected inflation. Hasan
(2009) also claimed that there is positive relationship between stock market
returns and inflation in United Kingdom and this isaligning to the hypothesis
ofFisher effect. Erdem and Arslan (2005) examined the relationship between
the index of Istanbul stock exchange and several macroeconomic variables.
Their findings show that there is a negative relationship between inflation, as
the proxy of Consumer Price Index (CPI), and the stock market returns.
According to the investigation done by Apergis and Eleftherio (2002), results
35
show that there inflation, which act as a proxy of Consumer Price Index (CPI),
has a great impact on the performance of The Stock Exchange of Athens
Bhattacharya and Mukherjee (2002) analyzed the fundamental relationship
between a set of macroeconomic variables, which inclusive of Consumer
Price Index (CPI), and SET Sensitive Index. The study employed of unit root
tests, co-integration and Granger Causality test. It is that there are
bilateralrelationship between stock market returns and inflation rate, as a
proxy of Consumer Price Index (CPI).
Choudhry (2000) studied the relationship between inflation and stock market
returns countries with high inflations and figure out that there is a positive
relationship among them.
Islam (2003) examined bothlong-run equilibrium relationships andshort-run
dynamic movement between inflation, as a proxy of Consumer Price Index
(CPI) and FTSE Bursa Malaysia (KLCI) Composite Index. From his study, he
concluded that there are significant short-run and long-run relationships
between inflation and stock returns of KLCI.
Maysami and Koh (2000) examined the relationship of inflation and stock
market returns in Singapore. From their findings, it is believed that inflation
has a co-integrating relation with the movement in Singapore’s stock market
returns.
Maysami et al., (2004) concluded in their study that there is a positive
relationship between stock market returns and inflation rate, as a proxy of
Consumer Price Index (CPI).The result isopposing to other studies that show
negative relationship between stock market returns and inflation. The reason
provided is that the active role of government in preventing hikes in prices
after 1997 financial crises and this is backed by the other studies conducted on
the Malaysian stock market (Ibrahim and Aziz, 2003).
36
The influence of Consumer Price Index (CPI) on stock market equity values in
Sri Lanka is investigated by Gunasekarage, Pisedtasalasai and Power (2004).
In the study observed 17-year period from January 1985 to December 2001
using monthly data series. They employed unit root tests and cointegration to
analyze both long-run and short-run relationships between the stock market
returns and Consumer Price Index (CPI). As a result, Unit Root test suggests
that the Consumer Price Index (CPI) andstock market returns are integrated of
order one. As for Johansen’s multivariate cointegration test’s results, itshows
there is long-run equilibrium relationship between Consumer Price Index
(CPI)the stock market returns. This indicates that minimum one cointegrating
relationship exists among the selected variables.
Liu and Shrestha (2008) studied the linkages between the index of Chinese
stock market and inflation, as a proxy of Consumer Price Index (CPI). They
employ heteroscedastic cointegration and they realized that inflation, as proxy
of Consumer Price Index (CPI) is negatively related with the index of Chinese
stock market.
As for Jordanian Stock market, Maghyereh (2002) investigated the long-run
relationship between inflation, as proxy of Consumer Price Index (CPI) and
stock market returns.Similarly to the other studies, Johansen’s co-integration
test is appliedand showed that it is reflected in stock market returns of
Jordanian equity market.
There is a research done by Anokye and Tweneboah (2008) in Ghana Stock
market, they analyzed both long-run and short-run relationships between
Consumer Price Index (CPI) and the stock market returns. They conclude that
there is cointegration between Consumer Price Index (CPI) and stock market
returns in Ghana, which shows the existence of long run relationship between
both variables.
Wong and Sharma (2002) investigated the relationship between Consumer
Price Index (CPI) and the stock market returns of Indonesia, Malaysia, the
37
Philippine, Singapore, and Thailand. Theyobserved both short and long run
relationships between Consumer Price Index (CPI) and the selected stock
markets. In their research, they noticed that Consumer Price Index (CPI)
andall the selected stock market returns were positively related in long run.
2.3.3 Exchange Rate (ER)
There are arguments of conventional economic models saying that changes in
exchange rates will lead to changes balance sheet items of a company through
its competitiveness as expressed in foreign currency. Eventually, company’s
profits will lead to price movements in the equity markets.The fluctuations in
price movements of thesecompanieswill bring impacts to the stock market
returns.
In the paper done by Rahman and Uddin (2009), theymeasured exchange rates
of US dollar in terms of Indian Rupee,Bangladeshi Taka and Pakistani Rupee
towards monthly data series of Bombay Stock Exchange Index,Dhaka Stock
Exchange General Index and Karachi Stock Exchange during the observation
period from January 2003 to June 2008. Their research result showed that
there is no cointegrating relationship between exchange rates and stock market
returns. Granger causality test showed that there is no causal relationship
between exchange rates and stock market returns in the selected countries.
Islam (2003) examined both long-run equilibrium relationships and short-run
dynamic movement between exchange rate and FTSE Bursa Malaysia (KLCI)
Composite Index. From his study, he concluded that there are significant
short-run and long-run relationships between exchange rate and stock returns
of KLCI.
38
The impact of exchange rate on stock market equity values in Sri Lanka is
investigated by Gunasekarage et al (2004). In the study observed 17-year
period from January 1985 to December 2001 using monthly data series. They
employed unit root tests and cointegration to analyze both long-run and shortrun relationships between the stock market returns and exchange rate.As a
result, Unit Root test suggests that exchange rate and stock market returns are
integrated of order one. As for Johansen’s multivariate cointegration test’s
results, itshows there is long-run equilibrium relationship between the
exchange rate the stock market returns. This indicates that minimum one
cointegrating relationship exists among the selected variables.
Liu et al (2008) studied the linkages between the index of Chinese stock
market and exchange rate. They employ heteroscedastic cointegration and
they realized that exchange rate is negatively related with the index of Chinese
stock market.
There is a research done by Anokye et al (2008) in Ghana Stock market, they
analyzed both short-run andlong-runrelationships between exchange rate and
stock market returns. They conclude and say that cointegration exists between
exchange rate and stock market returns in Ghana, which shows the presence
of long run relationship between both variables.
Wong et al (2002) investigated the relationship between exchange rate and the
stock market returns of Indonesia, Malaysia, the Philippine, Singapore, and
Thailand. Theyobserved both short and long run relationships between
exchange rate and the selected stock markets. In their research, they noticed
that exchange rate andall the selected stock market returns were positively
related in long run.
In the study of Nasrin and Hossain (2011), they used exchange rates to
analyze the stock market returns on DSE stock market of Bangladesh.
TheyusedGranger causality and cointegration tests to studyshort-run dynamics
and long-run equilibrium between the variables. The findingspropose that
39
there is dynamic causal link between the exchange rate and stock market
returns.
2.3.4 Gross Domestic Product (GDP)
Gross Domestic Product (GDP) is defined as the total value of final goods and
services produced within a country's in a year. It only measure final goods and
services that being consumed by final users. This is due to measuring
intermediate goods and services will possible lead to double calculation of
economic activity within a country in a year.
GDP calculation is contorted by inflation without any adjustment or
correction. GDP is attuned by dividing the nominal GDP (unadjusted GDP) by
a price deflator to arrive at the real GDP. Generally, nominal GDP is greater
than real GDP inan inflationary atmosphere. If the price deflator is unknown,
an implicit price deflator can be calculated by dividing the nominal GDP by
the real GDP (Reddy, 2012).
GDP growth rate is considered as a leading indicator measure of
macroeconomic performance as it has major impact on the unemployment
rates, CPI and other measures of an economy’s condition. Thus, people relate
many things in the market to GDP, and it is believed that higher GDP growth
rate will impact the stock market positively. It appears to be reasonable that
when an economy is expanding, companies within it are more likely to have
higher profits and will lead to bullish stock market at those times. However,
investigation shows that, at least in China, GDP growth rate does not translate
to stock price appreciation (Wu, 2012).
40
In the study done by Carstrom (2002), he concluded Gross Domestic Product
(GDP) and stock market returns are related. He explained that changes in
Gross Domestic Product (GDP) will lead to changes in the stock market
returns.
Further findings from Glen (2002), Taulbee (2001), Bilson (2001) and Ritter
(2005) show that Gross Domestic Product (GDP) is playingthe role as leading
indicator of stock market returns. Taulbee (2001) said that Gross Domestic
Product (GDP) actsas a proxy of the purchasing power ability and
hencehigher purchasing power ability will lead to greater performance of the
stock market.
There are also different viewpoints saying that stock market returns are not
linked to Gross Domestic Product (GDP) and this isbacked by study
conducted by Dimson, Marsh and Staunton (2005), using cross-sectional
analysis of stock market returns, they failed to identify any evidence to show a
positive relationship between Gross Domestic Product (GDP) and stock
market returns.
However, Chandra (2004) claimed that Gross Domestic Product (GDP)
growth rate is having positive relationship with the stock market returns.
Higher Gross Domestic Product (GDP) growth rate will lead to greater stock
market returns.
Kanakaraj, Singh and Alex (2008) investigated the linkages between Gross
Domestic Product (GDP) and the trend of stock market returnsfor time periods
from year 1997 to 2007. Their conclusion is there is a strong relationship
between the stock market returns and Gross Domestic Product (GDP).
Dimson, Marsh and Staunton (2002) investigated 16 countries with the data of
101 years and concluded that the stock market returns were negatively related
to Gross Domestic Product (GDP). On top of that, Professor Jay Ritter (2005)
had further investigated the data examined by Dimson, et al (2002) and he
also concluded that there is a negative relationship between stock market
41
returns and Gross Domestic Product (GDP).
2.3.5 Interest Rate (IR)
Interest rate relates directly to economic growth. It also brings impacts to the
stock market returns. Generally, interest rate is measured as the cost of capital,
which refers to the cost paid for the use of money for a period of time. Interest
rate is the borrowing rate (cost of borrowing money) from borrowers’ point of
view. However, for lender, theyjudge interest rate as the lending rate(fee
charged for) lending money (Alam and Uddin, 2009). In another words,
interest Rate is a rate that being charged for the use of money. Interest rate is
calculated by dividing the amount of interest applied by the amount of
principal. Interest ratemovement is derived from the changes inFederal
Reserve policies and the fluctuations of inflation.
Interest rate is playing an importantrole in deciding the amount of savings as
opposed to borrowing. If interest rate is low, people will reduce savings in
banks and invest more money in the stock markets; therefore it is presumed
that interest this may play an important role (Chandra, 2004).
Dritsaki (2005) investigated thelong run performance in Greek stock market
with interest rate by using Johansen cointegration approach and Granger
causality and his findings show that Greek stock market and interest are
having signification relationship.
According to Kevin (2000), interest rates are controlled within a preferred
range via monetary policy in an organized economy. However, in terms of
unorganized financial sector, interest rates are guided and could fluctuate
42
extensivelydependingon the demand and supply in the market. Chandra
(2004) suggested that an increase in interest rate will lead to reduction in
company’s profits as well as lead to an increase in the discount rate applied to
marker participants.Both scenarios will bring adverse impact on stock market
returns, and vice versa. Thus,greater interest rate is anticipated to bring
negative impact on company’s performance and eventually affect the stock
market returns.
According to Alam et al. (2009), there is a negative relationship between stock
market returns and interest rate. In the study, theyclaimed that when interest
rate increases, market participants willtransfer their funds from stock market
to banks. In contrary, when there is an increase in lending rate, market
participants will reduce their investments in the stock market and this will lead
reduce the investments in stock market and will eventually impact the stock
market returns.
Zafar, Urooj and Durrani (2008) suggested that interest rate will bring impact
to stock market returns.They explained that a rise in interest rate will lead to
an increase in the cost of investment.
Islam (2003) examined both long-run equilibrium relationships and short-run
dynamic movement between interest rate and FTSE Bursa Malaysia (KLCI)
Composite Index. From his study, he concluded that there are significant
short-run and long-run relationships between interest rate and stock returns of
KLCI.
Omran (2003) investigated the impact of interest rates in the movemnt of
Egyptian stock market. He applied the co-integration analysis via error
correction mechanisms (ECM) and it showed that there are significant longrun and short-run relationships between stock market returns and interest rate.
Islam and Watanapalachaikul (2003) investigated the stock market in
Thailand during year 1992 to 2001 and they concluded that there is a
significant long-run relationship between interest and stock market returns.
43
Interestingly, Maysami et al (2004) show that interest rate and stock market
returns are positively related in both short run and long run relationsips.
The impact of interest rate on stock market equity values in Sri Lanka is
investigated by Gunasekarage et al (2004). In the study observed 17-year
period from January 1985 to December 2001 using monthly data series. They
employed unit root tests and cointegration to analyze both long-run and shortrun relationships between the stock market returns and interest rate. As a
result, Unit Root test suggests that the interest rate and stock market returns
are integrated of order one. As for Johansen’s multivariate cointegration test’s
results, itshows there is long-run equilibrium relationship between interest rate
the stock market returns. This indicates that minimum one cointegrating
relationship exists among the selected variables.
Liu et al (2008) studied the linkages between the index of Chinese stock
market and interest rate. They employ heteroscedastic cointegration and they
realized that interest rate is negatively related with the index of Chinese stock
market.
There is a research done by Anokye et al (2008) in Ghana Stock market, they
analyzed both long-run and short-run relationships between interest rate and
the stock market returns. They concluode that there is cointegration between
interest rate and stock market returns in Ghana, which shows the existence of
long run relationship between both variables.
Wong et al (2002) investigated the relationship between interest rate and the
stock market returns of Indonesia, Malaysia, the Philippine, Singapore, and
Thailand. Theyobserved both short and long run relationships between interest
rate and the selected stock markets. In their research, they noticed that interest
rate andall the selected stock market returns were positively related in long
run.
44
2.3.6 Money Supply (M1)
Supply of money affects economic activities and that is why its control has
been the chief function of the central monetary authority of any given
economy (Osamwonyi, 2003). Kevin (2000) classifies the supply of money as
a leading indicator in stock market.
Money supply is the currency and other liquid instruments that flowing in a
country’s economy in a period of time. Money supply is one of the
mechanisms of monetary policy that used by the national central bank.
Changes in money supply can either be anticipated or unanticipated by the
people and they will bring different impacts to stock market respectively
(Maskay and Chapman, 2007).
Money supply iscategorized into severalgroups, which are M0, M1, M2 and
M3. However, different countries have different classification of money
supply (“Money Supply,” 2014). M0 and M1 refer to a narrow measure of
money’s function as a medium of exchange. M1 is inclusive in M2, which
acts as a wider measure and it reflects money’s function as a store of value.
M3 is even broader measure than M2 that covers items that regard as close
substitutes for money (Schwartz, 2008).
Shiblee (2009) provides a clearer picture of classification of money supply as
follow:
M0: M0 is the measure of money supply with the most liquidity. It includes
assets or cashthat can be transformed into currency in a short period of time.
This is known as narrow money because it is the smallest measure of the
45
money supply.
M1: M1 refers to M0 with demand deposits, which isthe checking account.
This is a measurement used by economists to calculate the amount of money
circulating in the economy. It is also a liquid measure of the money supply, as
it consists of assets and cash, which can be changed to currency easily.
M2: M2 is M1 with small time deposits, which refers to amount that less than
$100,000, non-institutional money-market funds andsavings deposits. M2 is a
wider classification of money than M1. M2 is widely used by economists
when looking to calculating the amount of money in the market. It also acts as
the key economic indicator that used to predict inflation.
M3: M3 is M2 plus institutional money-market funds,short-term repurchase
agreements, all large time deposits, along with other larger liquid assets. M3 is
the broadest measure of money and it is applied by economists to forecast the
entire money supply within an economy.
Shiblee (2009) also indicates that money supply can bring impacts directly to
When there is more money flowing in the economy, these will have direct
impact to the stock market due to this money willbe allocated to investments
(Shiblee, 2009). Vice versa, the money for investment will be lesser as well
when there is less money flowing in the economy.
According to Sirucek (2012), there is another important factor that will impact
the development of stock markets directly, which is money supply. Poire
(2000) also agree by indicating that stock return will show upward, when the
money supplies increase. And with the condition of money supply slips,
shares wil have lower trend.
There is an examination on such relationships in Singapore as well (Maysami
and Koh, 2000). These researchers found thata co-integrating relation with
changes in Singapore’s stock market levels can be formed by money supply
growth.
46
There is an examination on macroeconomics variables and stock market
interaction conducted by another researchers in New Zealand (Gan, Lee, Yong
and Zhang, 2006). In the studies, there is a set of seven macroeconomic
variables and used co-integration tests, Johansen maximum likelihood and
granger-causality tests. No evidence shows New Zealand Stock Index is a
leading indicator for changes in macroeconomic variables, however, in
general analysis, it was found that the NZSE40 is consistently determined by
money supply.
On other research that had been conducted by Nurazira and Daud (2010), they
had investigated the relationship between money supply and stock returns. In
this research, the result can either in short run or long run perspective. Hence,
in conclusion, the research had stated that the Malaysia stock market still in
the informational behavior. Nevertheless, this research only emphasizes in
1997 during the financial crisis strikes.
The impact of money supplyon stock market equity values in Sri Lanka is
investigated by Gunasekarage et al (2004). In the study observed 17-year
period from January 1985 to December 2001 using monthly data series. They
employed unit root tests and cointegration to analyze both long-run and shortrun relationships between the stock market returns and money supply. As a
result, Unit Root test suggests that the money supply and stock market returns
are integrated of order one. As for Johansen’s multivariate cointegration test’s
results, itshows there is long-run equilibrium relationship between money
supply the stock market returns. This indicates that minimum one
cointegrating relationship exists among the selected variables.
As for Jordanian Stock market, Maghyereh (2002) investigated the long-run
relationship between money supply and stock market returns.Similarly to the
other studies, Johansen’s co-integration test is appliedand showed that it is
reflected in stock market returns of Jordanian equity market.
47
Wong and Sharma (2002) investigated the relationship between money supply
and the stock market returns of Indonesia, Malaysia, the Philippine, Singapore,
and Thailand. Theyobserved both short and long run relationships between
money supply and the selected stock markets. In their research, they noticed
that money supply andall the selected stock market returns were positively
related in long run.
Bilson (2001) investigated the relationship between market performance in
emerging stock markets and money supply. According totheresult, money
supply shows to have linkages to stock market returns. Results presented by
Chena (2005) also suggested that stock market returns could explained by
money supply. Also, Theophano and Sunil (2006) applied bivariate VAR
models and concluded that stock market returns and money supply are
negatively related.
2.4 Proposed Theoretical Framework
Figure 1: Framework of factors affecting stock returns in the Financial Market of
Malaysia, Thailand, Indonesia and The Philippine from 2000-2014
48
Dependent Variable
Independent Variables
Consumer Price
Index (CPI)
Exchange Rate (ER)
Gross Domestic
Product (GDP)
Stock Return
(KLSE, SET, IDX, PSE)
Interest Rate (IR)
Money Supply (M1)
49
2.5 Conclusion
In conclusion, this paper studies both independent and dependent variables from past
researchers’ results in Chapter 2. Also, the review presented in this chapter will be
used to support the findings in this paper. Furthermore,this chapter also reviewed the
theoretical model and methodologies from past researchers. Lastly, this paper
proposed a theoretical framework for this paper.
50
CHAPTER 3
METHODOLOGY
3.0 Introduction
There are five macroeconomics variables used in this paper which are: Consumer
Price Index (CPI), exchange rate (ER), Gross Domestic Product (GDP), interest rate
(IR) and money supply (M1), and four stock markets, FTSE Bursa Malaysia (KLSE),
The Stock Exchange of Thailand (SET), Indonesia Stock Exchange (Bursa Efek
Indonesia, IDX) and The Philippine Stock Exchange (PSE).
The data of these variables are captured from January 2000 to December 2014, based
on quarterly basis. In this study, a total of 60 quarterly observations for each of the
variables are included. The reason of using quarterly data is to prevent reduction of
trading activity which is caused the reduction of buy or sell activities as well as price
limits of a stock market (Banerjee and Adhikary, 2007).
3.1 Research Design
51
Quantitative research has been used in this study. This means that this study will
involvedifferent types of empirical techniques. The empirical techniques here are
referring to thetechniques that using observation. As mentioned above, 60
observations that are retrieved from Datastream in order to construct the data set for
each of the dependent and independent variables. In another words, there is a
software called, E-views 8 software will be used in this paper to examine the
relationship between independent variables and dependent variable.
3.2 Data Collection Method
Since this study is using quantitative method as the examination method, there is no
primary data but only secondary data, which obtained from Datastream. A type of
data called time-series data will be used in this study. Due to the factor of the data is
readily available and less cost, secondary data is the main consideration of data for
this study. In addition, to serve the purpose of analyzing the data set for 15 years,
secondary data will definitely the main source of data as it can be examined over a
longer period of time.
3.2.1 Secondary Data
Sections above have indicated that the time-series data will be the period of
January 2000 to December 2014. This applicable to all of the data retrieved
from Datastream such as independent variables which are Consumer Price
52
Index (CPI), exchange rate (ER), Gross Domestic Product (GDP), interest rate
(IR), money supply (M1) as well as dependent variable which are FTSE Bursa
Malaysia (KLSE), The Stock Exchange of Thailand (SET), Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and The Philippine Stock Exchange
(PSE).
Table 1: Source of Data
Variables
Proxy
Explanation
Units
Source of
Data
Stock
Market
Returns
KLSE
SET
IDX
PSE
Stock Exchange of Malaysia
Stock Exchange of Thailand
Stock Exchange of Indonesia
Stock Exchange of Philippines
Index
Datastream
Country's Consumer Price Index
(as measure of inflation)
Currency exchange rate with US
Dollar (USD)
Index
Number
Percentage
(%)
GDP
Country's Gross Domestic Product
Index
Number
IR
Country's fixed deposit rate
M1
Country's money circulation of
category 1
Consumer
Price Index
Exchange
Rate
Gross
Domestic
Product
Interest
Rate
Money
Supply
CPI
ER
53
Percentage
(%)
Money
Currency
Datastream
Datastream
Datastream
Datastream
Datastream
3.3 Sampling Design
3.3.1 Target Population
The target population for this study is the stocks markets, which are FTSE
Bursa Malaysia (KLSE), The Stock Exchange of Thailand (SET), Indonesia
Stock Exchange (Bursa Efek Indonesia, IDX), and The Philippine Stock
Exchange (PSE). From the target population, researchers will get to know that
this study is targeting on those developing countries such as Malaysia,
Bangkok, Indonesia and Philippines, This study will help to estimate the
relationship between macroeconomic variables and stock return in the
developing country.
3.3.2 Sampling Element – Formula
3.3.2.1 FTSE Bursa Malaysia (KLSE)
54
FTSE Bursa Malaysia is a stock market index that was introduced in
1986. The main responsibility of KLCI is to become accurate
performance indicator on Malaysia Stock Market. In KLCI, it does
include 100 largest and multiple companies that come from main
board by full market capitalization companies from Bursa Malaysia.
In order to calculate the FTSE Bursa Malaysia KLCI, real time and
closing price have been retrieved from Bursa Malaysia. Value
weighted formula is the calculation method andit will then adjusted by
a free float factor. To get a real time information, calculations will be
done on every 15 seconds (‘FTSE Bursa Malaysia KLCI’, 2011).
The formula below shows the calculation of FTSE Bursa Malaysia
KLCI:
Σ[(pn1*en1)*sn1*fn1*cn1] / d
Where:
1.) n representing the number of securities. There are 30 securities in
KLCI.
2.) P referring to the latest trade price of the component security. In
order to convert the securities’ home currency to index based security,
e is the correct indication, as it represent the currency rate.
3.) s is the number of share in issue used by FTSE for the security.
4.) In order to allow any amendment on weighting, FTSE introducing
the free float factor (f) for each of the security. This will help to
55
express the difference between 0 and 1, where 1 represents 100 percent
free float.
3.3.2.2 The Stock Exchange of Thailand (SET)
In order to respond to the development of capital market which take
place at different periods of time and investors’ demand, Index Series
has been created by The Stock Exchange of Thailand. This index
series will serves the purpose as indicator of price movements for
securities traded on SET. Besides that, this can be the benchmark for
investment performance and as index indicator for derivatives, mutual
funds and exchange-traded fund (ETF). Most importantly, the index
series, which owned by SET is the trademark of it (“History and
Roles”, 2015).
According to the pre-defined criteria, there are few components in
SET Index Operation Framework. These components are index
calculation and dissemination, index information service, and selection
of index constituents. With the internal audit guidance, SET has
concluded the operational criteria and guidelines in order to ensure
accuracy and continuation of index dissemination (“History and
Roles”, 2015).
In the event where major adjustments such as listed companies,
brokerage firms and asset management companies are required for
56
SET Index Series’ Ground Rules, SET will ask for justification and
reason of change from stakeholders. This is because these types of
adjustments will have significant impact on the SET’s stakeholders.
With this in replace, SET will announce in advance for the changes to
be effective (“History and Roles”, 2015).
Calculation of Index by SET is based on the Price Index and Total
Return Index (“History and Roles”, 2015).
In general, Price Index is an index, which reflects price movement of
securities. Weighted average market capitalization index is the index
that created by SET. In addition, SET Index Series and mai Index
Series are the criteria of the price index (“History and Roles”, 2015).
Price Index formula:
𝐼𝑛𝑑𝑒𝑥
=
(𝐶𝑢𝑟𝑟𝑒𝑛𝑡𝑀𝑎𝑟𝑘𝑒𝑡𝑉𝑎𝑙𝑢𝑒
/
𝐵𝑎𝑠𝑒𝑀𝑎𝑟𝑘𝑒𝑡𝑉𝑎𝑙𝑢𝑒)
×
𝐵𝑎𝑠𝑒𝑉𝑎𝑙𝑢𝑒
3.3.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Different from other index, all Listed Companies are the criteria of
Jakarta Composite Index (JCI) in order to construct the index
calculation. Indonesia Stock Exchange is the only responsible party to
reduce one or more Listed Companies from the calculation. This is to
ensure JCI will shows the real and fair market condition. In order for
57
the reduction to take place, there is a condition to be fulfilled. If Listed
Company’s public shares only owned by a few shareholders (small
free float) and at the same time, the market capitalization is high, the
reduction may take place. Due to price change of Listed Company’s
stock, there might be an impact on normal fluctuation of the JCI (“The
Capital Market”, 2010).
JCI is the index that owned by Indonesia Stock Exchange. However,
Indonesia Stock Exchange is not liable for the products that offered by
users who use JCI as their benchmark. Besides, Indonesia Stock
Exchange is not accountable for any investment decisions made by any
Parties that use JCI as a benchmark (“The Capital Market”, 2010).
The LQ45 Index, which was launched in February 1997, is a market
capitalization-weighted index that captures the performance of 45 most
liquid companies listed on the Indonesia Stock Exchange (the “IDX”).
The LQ45 Index covers at least 70% of the stock market capitalization
and transaction values in the Indonesia Stock Market. The Index is
denominated in Indonesia rupiah (“IDR”) and is published throughout
the trading hours of the IDX (“The Capital Market”, 2010).
The basis for the LQ45 Index's calculation is the Aggregate Market
Value of the total listed stocks on 13July 1994. The Aggregate Market
Value is the total of the multiplication of each listed shares (excluding
the shares of companies under restructuring program) with each price
in the IDX on that day.
The calculation formula is as follows:
LQ45 = Market Value/Base Value x 100
In order to ensure that the index calculation still represents the
58
movement of the stock price in the market occurring in a continuous
auction trading system, the Base Value is immediately adjusted if there
is a change in the issuer's capital or other factors which do not relate to
the stock price. The adjustment will be done if there is an addition of
new issuer, rights offering, partial/company listing, new stock issuance
originating from warrants and convertible bonds as well as delisting.
In the case of stock split, stock dividend or bonus issue, the Base
Value is also adjusted though Market Value is not influenced (“The
Capital Market”, 2010).
The stock price currency used for calculating the LQ45 Index is the
currency at the regular market, in which the occurring transaction is
based on the continuous auction market.
The formula for adjusting the Base Value is:
New Base Value = (Old Market Value + New Stock Market Value) /
Old Market Value x Old Base Value
3.3.2.4 The Philippine Stock Exchange (PSE)
PSE, six sector indices and the All Shares Index are the criteria that
construct the PSE index series. All these indices are free float adjusted,
with the exclusion of the All Shares Index(“The Philippines Stock
Exchange”, 2012).
The PSE is the main index of the Exchange. There are 30 companies
will be selected base on specific criteria for this index. Changes in free
59
float-adjusted market capitalization will be measured by PSE. The
measurement will be performed on most active common stocks and
capitalization of 30 largest companies that are listed at PSE.As a
result, PSE will be able to provide an overview of the market condition
by referring to the changes in the stock prices for those listed
companies.There is a result saying that 1,022.045 points is the base
level of PSE. Reconciliation was performed based on the PSE’s base
date, which is also the date of close of index on February 28, 1990
(“The Philippines Stock Exchange”, 2012).
In year 2016, April, naming of PSE was retrieved from Exchange.
Previously, Phisix and the PSE Composite Index were the various
labels that used tolabel the Exchange’s main index(“The Philippines
Stock Exchange”, 2012).
In order to track the performance of the particular sectors in market,
sector indiceswill be used. From the sector indices, there are six types
of indices that representing each major sector under revised industry
classification of the exchange. The six indices that are maintained by
PSE are including Financials Index, Services Index, Property
Index,Industrial Index, Mining & Oil Index and Holding Firms
Index(“The Philippines Stock Exchange”, 2012).
A complementary index to the PSE called, All Shares Index. This
index contains all common stocks of companies listed at the Exchange,
hence, it is considered asa broader barometer of the index. On
November 14, 1996, the base value of the All Shares Index was
defined as 1,000.00 points. To calculate the All Shares Index, full
market capitalization method will be used in this study, by excluding
those listed in the Small and Medium Enterprises (SME) Board (“The
Philippines Stock Exchange”, 2012).
60
Computing of PSE index series are through the PFI 2 (Platform
Indices 2). It is application used to calculate indices in the New
Trading System (NTS) of the PSE (“The Philippines Stock Exchange”,
2012).
With the computer terminals that linked to NTS, index levels that are
calculated by PFI 2 will be displayed and broadcasted to members and
data vendors. In addition, television, radio, PSE website as well as
other website that provide reports on performance of the stock market
can be the way to monitorthe index. Lastly, trading’s final result will
be published in the PSE Daily Quotation Report, as well as in major
newspapers (“The Philippines Stock Exchange”, 2012).
The computation of the index will be initiated by deriving the change
in the index components’ current total free float-adjusted market
capitalization from the base total free float-adjusted market
capitalization, and multiplying this change with the previous day’s
closing index level. The base total free float-adjusted market
capitalization is the sum of all the products of the index stocks’
previous day’s last traded price and their current free float shares
(“The Philippines Stock Exchange”, 2012).
Below is the formula for computing the index:
PSE Indext = [Σ n i=1 (Pi x Si x Fi ) x PSE Indext-1] / b x PSE
Indext-1
i = 1,2,3,…,n
Where:
61
n = Number of constituents of the index
Pit = Last traded price of company i at day t
Sit = Number of outstanding shares of company i at day t
Fit = Free float factor of company i to be applied to each security,
expressed as a number between zero to 1, where 1 represents 100%
free float.
b = Base free float-adjusted market capitalization.
The base free float-adjusted market capitalization will be adjusted
when there are conditions of stock splits, reverse stock splits, stock
dividend declarations, stock rights offerings, or other corporate
actions. These corporate actions will result the adjustments in a
company’s previous day’s last traded price and/or free float factor.
3.3.3 Sampling Technique
To analyze the data, E-views 8 will be the tool that used in this study. This
tool will be used in the study due to it is a simple and user-friendly
econometrics program that provides data analysis, forecasting and estimating
tools. In practical econometrics, this is the most frequently tool used. Besides
62
the advantages mentioned above, E-views also provided the advantage for
visual features of modern windows software.
There are few analyses can be performed by using E-views 8, such as
Variance
Decomposition,
Unit
Root
test,
Ordinary
Least
Square
(OLS),JohansenCointegration Tests, Impulse Response Function and Granger
Causality Tests.
To examine the presence of stationarity, Phillips-Perron (PP), Augmented
Dickey-Fuller (ADF) test and Unit Root Test will be used in this study.For the
reason of combining ideas and opinions from past studies and to fit these
methods into this study, the above mentioned model will be employed in this
study to perform the analyses.According to the studies from various
researchers, the models of Johansen Cointegration, Impulse Response
Function, Granger Causality, Unit Root test and Variance Decomposition are
suitable and recommended for this study in order to examine the relationship
between the dependent and independent variables.
3.3.4 Sampling Size
By referring to the quarterly period of January 2000 to December 2004,
sampling size for this study can be derived. Each of the variables will have 60
quarterly observations throughout this paper.
63
3.4 Data Processing
Figure 2: Data Processing Diagram
Collect data from secondary
sources.
Rearrange, edit and calculate
the data
Analyze calculated data using Eviews 8
Interpret the results and findings
that generated from E-views 8
There are four steps for data processing, according to the figure above. First of all,
Datastream will be the tool to collect data. The collected data will then be rearranged,
edited and calculated. Next, data will be analyzed via E-views 8. Lastly, the result and
findings will be interpreted in this study.
64
3.5 Multiple Regression Model
As a data analysis technique, multiple regression will be used to investigate the
significance relationship of a dependent variable to independent variables (Berger,
2003). The return data can be calculated in quarterly basis, by transforming the
variables into natural logarithm (Kandir, 2008). Through this step, the gap of the data
between variables can be reduced.
Economic Function
Stock Market = f (Consumer Price Index, Exchange Rate. Gross Domestic Product,
Interest Rate, Money Supply)
Economic Model
Log(Stock Market)t = β0 + β1Log(CPI) + β2Log(ER) + β3Log(GDP) + β4Log(IR) +
β5Log(M1) + ɛt
N=60 observation
Where,
Log(Stock Market) = Natural logarithm of stock market return in a particular country
at t year
Log(CPI) = Natural logarithm of Consumer Price Index of a particular country at t
year
65
Log(ER) = Natural logarithm of exchange rate of a particular country at t year
Log(GDP) = Natural logarithm of Gross Domestic Product of a particular country at t
year
Log(IR) = Natural logarithm of interest rate of a particular country at t year
Log(M1) = Natural logarithm of money supply category 1 of a particular country at t
year
3.6 Hypotheses of the Study
3.6.1 Consumer Price Index (CPI)
H0: There is no relationship between the stock returns of emerging countries
in Southeast Asia and Consumer Price Index (CPI).
H1 : There is a relationship between the stock returns of emerging countries in
Southeast Asia and Consumer Price Index (CPI).
66
3.6.2 Exchange Rate (ER)
H0: There is no relationship between the stock returns of emerging countries
in Southeast Asia and exchange rate (ER).
H1 : There is a relationship between the stock returns of emerging countries in
Southeast Asia and exchange rate (ER).
3.6.3 Gross Domestic Product (GDP)
H0: There is no relationship between the stock returns of emerging countries
in Southeast Asia and Gross Domestic Product (GDP).
H1 : There is a relationship between the stock returns of emerging countries in
Southeast Asia and Gross Domestic Product (GDP).
3.6.4 Interest Rate (IR)
H0: There is no relationship between the stock returns of emerging countries
in Southeast Asia and interest rate (IR).
67
H1 : There is a relationship between the stock returns of emerging countries in
Southeast Asia and interest rate (IR).
3.6.5 Money Supply (M1)
H0: There is no relationship between the stock returns of emerging countries
in Southeast Asia and money supply (M1).
H1 : There is a relationship between the stock returns of emerging countries in
Southeast Asia and money supply (M1).
3.7 Data Analysis
To investigate the relationship between independent variables and dependent variable,
certain tests have been identified such as Unit Root test, Granger Causality, Ordinary
least square, Johansen Cointegration, Impulse Response Function and Variance
Decomposition.
68
3.7.1 Ordinary least square (OLS)
To model a single response variable that recorded on at least an interval
scales, Ordinary least-squares (OLS) regression, which is a generalized linear
modeling technique will be used in this study.This technique may be applied
to categorical explanatory variables that have been appropriately coded and
also single or multiple explanatory variables.
According to the study by Hoyt (2003), Ordinary Least Square (OLS) is a
statistical technique that uses sample data for the estimation of true population
relationship between two variables. Before proceeding to any analyses,
Ordinary Least Square (OLS) will be the first model to test the economic
equation that suggested in this paper. Through the application of Ordinary
Least Square (OLS), economic problems can be detected and identified. To
identify the economic problems such as autocorrelation, model specification
error and heteroscedasticity, some of the techniques will be used in this study.
When the error terms do not have constant variables, the problem of
Heteroscedasticity
will
occur.
Through
Probability of
F-
statistic,
Heteroscedasticity can be detected (Stock and Watson, 2006).
According to Stock and Watson (2006), the condition of residuals are related
to each other will be defined as problem of autocorrelation and it can be
confirmed from Probability of Chi-Square.
Lastly, there are several types of model specification error such as inclusion of
unnecessary variables, incorrect functional forms, omission of relevant
69
variables and others(Gujarati and Porter, 2009).
3.7.2 Unit Root Test
In order to determine whether trending data should be first differenced or
regressed on deterministic functions of time, Unit root tests will be used to
render the data stationary. The existence of long-run equilibrium relationships
among nonstationary time series variables is proposed by Economic and
finance theory. If these variables are I(1), then cointegration techniques can be
used to model these long-run relations. Hence, pre-testing for unit roots is
often a first step in the cointegration modeling.
Unit roots tests serve the purpose of establishing the order of integration of
each variable. Through analyzing the stationary properties of those variables
by applying the unit root, will then be able to analyze the effects of the
selected macroeconomic variables on the selected stock markets of emerging
nations.
A
statistical
property is
a stationary time
series
including
autocorrelation, variance, and others are constant over time. There will be
lesser spurious regression, when stronger stationarity occurred.To test the
presence of unit root and stationarity of each variable in this paper,
Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test are the test
to be used in this study (Gan et al., 2006).
According to some previous researches, such as Nopphon (2012) and Sari and
Soytas (2006), the non-stationary data can create spurious result due to invalid
70
analysis. To ensure the validity of analysis, the augmented Dickey-Fuller test
of unit root (Dickey and Fuller, 1979) is conducted. Besides, it serves the
purpose of examine on the coefficient of the regression. ADF consists a
running regression of the first difference of the series against the lagged
difference terms,series lagged once and optionally, a constant and a time trend
(Al-Zoubi and Al-Sharkas, 2011). Alternatively, in order to avoid spurious
regressions that arise due to carrying out regressions on time series data
without subjecting them for test whether they contain unit root by using Eviews, ADF test will be used (Asaolu and Ogunmuyiwa, 2011). However,
ADF test has poor power properties is the weakness (Paramaia and Akway,
2008).
Phillips-Perron (PP) test is conducted in a similar manner by using regression,
without the lagged first differenced terms. There is similarity with ADF test
but it has a difference of automatic correlation was incorporated to DF
procedure and controls the higher-order serial correlation. A non-parametric
statistical method used for PP Test and the use of adding lagged difference
terms can be avoided in ADF test (Asmy et al., 2009).
3.7.3 Johansen Cointegration
The cointegration properties of the data series continue to be assessed, as long
as order of the integration is established for each variable.To determine
whether the linear combination of the series contains long run equilibrium
relationship, Johansens co-integration test will be conducted. Besides,
71
Johansen & Juselius cointegration test is performed in order to determine
whether the linear combination of the series contains long run equilibrium
relationship. Furthermore, the relationship between dependent variable and
independent variable in short run or long run period can be explained by
Johansens co-integration test (Ali et al., 2010). Generally, if a set of variables
is individually non-stationary and integrated of the same order, yet their linear
combination is stationary, it will be said as cointegrated (Ibrahim, 2000). The
dependent and independent variables move closely together in the long run is
the basic idea of cointegration(Azizan and Sulong, 2011).
The data from a linear combination of two variables can be stationary will be
defined as Cointegration. If there is at least one is cointegrating relationship
among the variables, by estimating the vector error-correction models
(VECM), then the causal relationship among these variables can be
determined. For this purpose, a Johansen method of multivariate cointegration
will be used (Asmy et al., 2009). To examine the number of cointegrating
vectors in the model,the Johansen maximum likelihood method from Johansen
and Juselius (1990) is utilized (Chin and Jayaraman, 2007). To test the whole
system in one step, Johansen vector error-correction models (VECM) is a full
information maximum likelihood estimation model that suitable (Maysami et
al, 2004).
3.7.4 Granger Causality
In the absence of any cointegration relationship between the above variables,
72
Granger causality tests would be applied. In the year of 1969, in order to
determine causality between two time series and whether one time series is
useful in forecasting another, Granger Causality has been proposed by Clive
Granger (Harasheh and Abu-Libdeh, 2011). For the purpose of testing on
short run relationship between dependent and independent variables, Granger
Causality test will be used. To test the existence of short run relationship,
stationary data is more important than non stationary data. In this technique,
the methodology is sensitive to lag length used for the investigation of
stationary property of data.
There is an examination relationship between the dependent and independent
variables proposed by Granger (Ali et al., 2010). To analyze the relationship
between stock market returns and macroeconomic variables in different
countries around the world, this method is popular as most of the previous
researches used this technique (Granger, Huang and Yang, 1998; Ali et al.,
2010). For instance, Gan et el. (2006) used this method to examine whether
there are lead-lag relationship between NZSE returns and the selected
macroeconomic variables. The examination on Mexico’s stock prices lead to
exchange rates in the short run and there is no long run relationship between
them was conducted by Kutty (2010) by using Granger causality test.
However, Granger causality tests are inappropriate when the variables were
being analyzed as a non-stationary and cointegrated (Ibrahim, 2000). To
capture the long run and short run causal dynamics in terms of interactive
73
feedbacks (lead-lag relationships) among the variables, relevant vector errorcorrection models (VECM) are estimated (Agrawalla and Tuteja, 2008). Last
but not least, error-correction term is included in an augmented form of
Granger causality test (Shahbaz, Ahmed and Ali, 2008).
3.7.5 Variance Decomposition
A substantial part of the variation in stock market returns over the short and
medium-run, namely, one, four and eight years can be explained by
macroeconomic variables, which is Variance decomposition. Vector auto
regression (VAR) with orthogonal residuals will constructthe Variance
Decompisition. The contribution of macroeconomic variables in forecasting
the variance of stock market returns can be expressed (Kazi, 2008).
According to Okuda and Shiiba (2010), the examination of the information
content of several earnings components can be done by variance
decomposition methodology, which is a complementary approach. For
instance, the accruals and show that news associated with accruals, cash
flows, and expected future returns are included when there is extension of
variance decomposition framework (Callen and Segal, 2004).These are the
important aspects in driving stock market returns. In addition, Variance
decomposition has been used by other researchers in examining the relative
importance of the various forecasting variables in causing unexpected stock
returns (Sari and Soytas, 2006)
74
3.7.6 Impulse Response Function
In order to investigate the short run dynamic linkages between NZSE40 and
macroeconomic variable throughout the testing period, Impulse Response
Function can be used (Gan et al., 2006). By regressing the series of interest on
estimated innovations, which are the residuals obtained from a prior-stage
‘long auto regression’,the impulse responses can be (Chang and Sakata,
2007). Typical orthogonalization and ordering problems can be avoided
through this methodology, which would be hardly feasible in the case of
highly interrelated financial time series observed at high frequencies
(Panopoulou and Pantelidis, 2009).
Furthermore, with a stationary time series, the impulse response functions are
only reliable. After the second difference, data will turn into stationary after.
To examine the short-run impact caused by the vector auto regression model
(VECM) when it received certain impulses, this act as an econometric
technique. With the conditions of time varying second moments, these
approaches also provide a system consistent solution for multivariate linear
autoregressive models (Elder, 2003).
For instance, in order to check the existence of short run relationship between
stock market returns and macroeconomic variables, Impulse response function
has been chosen (Philinkus and Boguslauskas, 2009).
75
3.8 Conclusion
In conclusion, there are five macroeconomic variables; Consumer Price Index (CPI),
Exchange Rate (ER), Gross Domestic Product (GDP), interest rate (IR) and money
supply (M1) are selected to examine the relationships with stock market returns in
Malaysia, Thailand, Indonesia and The Philippines. There are 60 quarterly
observations for each variable from January 2000 to December 2014. Data were
collected from Datastream. Testing models introduced in this paper are Ordinary
Least Square (OLS), Unit Root Test, Johansen Cointegration Test, Granger Causality
Test, Variance Decomposition and Impulse Response Function.
76
CHAPTER 4
FINDINGS AND ANALYSIS
4.0 Introduction
As mentioned in previous chapter, testing models such as Ordinary Least Square
(OLS), Unit Root test, Johansen Cointegration, Granger Causality, Variance
Decomposition and Impulse Response Function will be performed. Results and
finding will be presented and interpreted in this Chapter.
4.1 Descriptive Statistics
4.1.1 FTSE Bursa Malaysia (KLSE)
Table 2: Descriptive Statistic of Variables for Log(KLSE)
LOG(KLCI) LOG(CPI) LOG(ER) LOG(GDP)
Mean
6.987007 4.536724 1.249676 11.93168
Median
6.929862 4.528823 1.263888 11.96734
Maximum
7.532779 4.714921 1.335001 12.50176
Minimum
6.361317 4.386185 1.099223
11.3068
Std. Dev.
0.345828 0.10364 0.084693 0.376973
Skewness
0.011304 0.073454 0.342366 -0.175613
Kurtosis
1.712507 1.608168 1.533908
1.66352
77
LOG(IR)
1.752558
1.792591
2.050699
1.504818
0.162317
0.009146
1.78164
LOG(M1)
11.92259
11.92793
12.70202
11.13748
0.497414
0.017291
1.718964
Jarque-Bera
Probability
Observations
4.145376 4.896943 6.545708
0.125847 0.086426 0.037898
60
60
60
4.773846 3.711842 4.105624
0.091912 0.156309 0.128373
60
60
60
Table 2 is showing the descriptive statistics of the independent and dependent
variables that being analyzed for FTSE Bursa Malaysia (KLSE). Stock market
returns, Consumer Price Index (CPI) and money supply (M1) are positively
skewed which show that they asymmetrical. Kurtosis values of the variables
are deviated from and this shows that the data is not normally distributed.
4.1.2 The Stock Exchange of Thailand (SET)
Table 3: Descriptive Statistic of Variables for Log(SET)
LOG(BSE) LOG(CPI) LOG(ER)
LOG(GDP) LOG(IR)
LOG(M1)
Mean
6.536743 4.486985 3.586569 13.81641 1.894244 6.829954
Median
6.552409 4.491927
3.55201 13.85293 1.924249 6.811643
Maximum
7.355091 4.679535 3.816026 14.05667 2.079442 7.395046
Minimum
5.609411 4.306495 3.388394 13.50751 1.704748 6.134771
Std. Dev.
0.499431
0.12443 0.132171 0.173209 0.107693 0.379315
Skewness
-0.160126 0.007192 0.161566 -0.336318 -0.496392 -0.180849
Kurtosis
2.089608 1.595939 1.570039 1.862082
2.15103 1.927592
Jarque-Bera
2.328439 4.928984 5.373005 4.368238 4.265926 3.202213
Probability
0.312166 0.085052 0.068119 0.112577 0.118486 0.201673
Observations
60
60
60
60
60
Table 3presents the descriptive statistics for the dependent and independent
variables that being analyzed for The Stock Exchange of Thailand (SET).
Consumer Price Index (CPI) and Exchange Rate (ER) are positively skewed
which show that they asymmetrical. Kurtosis values of the variables are
78
60
deviated from and this shows that the data is not normally distributed.
4.1.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 4: Descriptive Statistic of Variables for Log(IDX)
LOG(IDX) LOG(CPI) LOG(ER)
LOG(GDP) LOG(IR)
LOG(M1)
Mean
7.321128 4.278051
9.15997 13.09737 2.674854 12.78631
Median
7.450659 4.322132 9.132433 13.08568 2.631528 12.82674
Maximum
8.540853 4.747364 9.404632 13.52191 2.974679 13.75401
Minimum
5.93843 3.676132 8.921324 12.73924 2.436825 11.72059
Std. Dev.
0.887012
0.31826 0.101396 0.236654 0.163961 0.617671
Skewness
-0.196946 -0.317112 0.827132 0.129047 0.408243 -0.012381
Kurtosis
1.600951 1.848786 3.386322 1.758669 1.891065 1.710808
Jarque-Bera
5.281224 4.318839 7.214585 4.018786 4.740969 4.156576
Probability
0.071318 0.115392 0.027125
0.13407 0.093435 0.125144
Observations
60
60
60
60
60
Table 4 shows the descriptive statistics for the dependent and independent
variables that being analyzed for Indonesia Stock Exchange (Bursa Efek
Indonesia, IDX). Exchange Rate (ER), Gross Domestic Product (GDP) and
Interest Rate (IR) are positively skewed which show that they asymmetrical.
Kurtosis values of the variables are deviated from and this shows that the data
is not normally distributed.
4.1.4 The Philippine Stock Exchange (PSE)
Table 5: Descriptive Statistic of Variables for Log(PSE)
79
60
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability
Observations
LOG(MSE) LOG(CPI) LOG(ER)
LOG(GDP) LOG(IR)
LOG(M1)
7.860044 4.644645 3.865954 14.01604 2.125572 13.59772
7.806795 4.633256 3.860253 14.03144 2.174745 13.62944
8.880229
4.94805 4.029243 14.39847 2.575154
14.5823
6.939432 4.323249 3.706678 13.67576 1.698669 12.72432
0.582296 0.195274 0.101662
0.21522 0.246683 0.572478
0.1957 -0.043711 0.152352 0.089226 -0.397746 0.086978
1.862221 1.624425 1.687692 1.846536 2.102691 1.734596
3.61934 4.749623 4.537489 3.405813 3.594923 4.078769
0.163708 0.093032 0.103442 0.182153 0.165719 0.130109
60
60
60
60
60
Table 5 shows the descriptive statistics for the dependent and independent
variables that being analyzed for The Philippine Stock Exchange (PSE). Stock
market returns, Exchange Rate (ER), Gross Domestic Product (GDP) and
money supply (M1) are positively skewed which show that they
asymmetrical. Kurtosis values of all variables show that the data is not
normally distributed as the values of Kurtosis are deviated from 3.
4.2 Ordinary Least Square (OLS)
4.2.1 FTSE Bursa Malaysia (KLSE)
Table 6:Log(KLSE) is explained by Log(CPI). Log(ER), Log(GDP), Log(IR)
and Log(M1)
Dependent Variable: LOG(KLSE)
Method: Least Squares
Date: 08/09/15 Time: 02:46
80
60
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
4.135989
-3.093987
-0.963392
-1.056170
1.410948
2.366988
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.947681
0.942836
0.082684
0.369176
67.58844
195.6249
0.000000
Std. Error
t-Statistic
2.371906 1.743741
0.969224 -3.192233
0.296582 -3.248314
0.252321 -4.185816
0.236705 5.960773
0.304980 7.761133
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
81
Prob.
0.0869
0.0024
0.0020
0.0001
0.0000
0.0000
6.987007
0.345828
-2.052948
-1.843514
-1.971027
0.732732
4.2.2 The Stock Exchange of Thailand (SET)
Table 7: Log(SET) is explained by Log(CPI). Log(ER), Log(GDP), Log(IR)
and Log(M1)
Dependent Variable: LOG(SET)
Method: Least Squares
Date: 08/09/15 Time: 01:18
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-3.214831
-3.579738
-0.340678
0.986981
0.450098
1.836987
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.877671
0.866344
0.182587
1.800254
20.05620
77.48624
0.000000
Std. Error
t-Statistic
9.303501 -0.345551
1.552937 -2.305140
0.483235 -0.704994
0.796930 1.238480
0.340546 1.321698
0.494418 3.715457
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
82
Prob.
0.7310
0.0250
0.4838
0.2209
0.1918
0.0005
6.536743
0.499431
-0.468540
-0.259105
-0.386619
0.613729
4.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 8:Log(IDX) is explained by Log(CPI). Log(ER), Log(GDP), Log(IR)
and Log(M1)
Dependent Variable: LOG(IDX)
Method: Least Squares
Date: 08/09/15 Time: 01:18
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-7.028513
0.629930
-1.068789
1.485780
-0.953241
0.354660
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.963165
0.959754
0.177946
1.709903
21.60092
282.3996
0.000000
Std. Error
t-Statistic
10.24108 -0.686306
0.539759 1.167059
0.275682 -3.876886
1.354163 1.097194
0.354217 -2.691125
0.703023 0.504479
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
83
Prob.
0.4955
0.2483
0.0003
0.2774
0.0095
0.6160
7.321128
0.887012
-0.520031
-0.310596
-0.438109
0.284795
4.2.4 The Philippine Stock Exchange (PSE)
Table 9:Log(PSE) is explained by Log(CPI). Log(ER), Log(GDP), Log(IR)
and Log(M1)
Dependent Variable: LOG(PSE)
Method: Least Squares
Date: 08/09/15 Time: 01:17
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-49.33752
-2.676674
-1.617534
5.877945
0.090379
-0.492329
8.301413
0.916703
0.236964
0.908947
0.190110
0.397412
-5.943268
-2.919893
-6.826087
6.466765
0.475404
-1.238837
0.0000
0.0051
0.0000
0.0000
0.6364
0.2208
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.951713
0.947242
0.133748
0.965981
38.73236
212.8630
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
84
7.860044
0.582296
-1.091079
-0.881644
-1.009157
0.713650
4.3 Diagnostic Checking
4.3.1 Autocorrelation
Hypothesis:
H0: There is no autocorrelation problem.
H1: There is an autocorrelation problem.
Decision rules:
1.) We do not reject H0 if P-value of the Chi-squared > 0.01, this means that
there is no autocorrelation problem.
2.) We reject H0 if P-value of the Chi-squared < 0.01, this means that there is
an autocorrelation problem (Stock and Watson, 2006).
85
4.3.1.1 FTSE Bursa Malaysia (KLSE)
Table 10: Breusch-Godfrey Serial Correlation LM Test (KLSE)
F-statistic
Obs*R-squared
21.76274
27.33856
Prob. F(2,52)
Prob. Chi-Square(2)
0.0000
0.0000
P-value of Chi-square is 0.0000 < 0.01. Thus, we reject H0 and it is
believed that there is an autocorrelation problem.
4.3.1.2 The Stock Exchange of Thailand (SET)
Table 11: Breusch-Godfrey Serial Correlation LM Test (SET)
F-statistic
Obs*R-squared
31.48747
32.86365
Prob. F(2,52)
Prob. Chi-Square(2)
0.0000
0.0000
P-value of Chi-square is 0.0000 < 0.01. Thus, we reject H0 and it is
believed that there is an autocorrelation problem.
86
4.3.1.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 12: Breusch-Godfrey Serial Correlation LM Test (IDX)
F-statistic
Obs*R-squared
52.65282
40.16600
Prob. F(2,52)
Prob. Chi-Square(2)
0.0000
0.0000
P-value of Chi-square is 0.0000 < 0.01. Thus, we reject H0and it is
believed that there is an autocorrelation problem.
4.3.1.4 The Philippine Stock Exchange (PSE)
Table 13: Breusch-Godfrey Serial Correlation LM Test (PSE)
F-statistic
Obs*R-squared
20.47167
26.43116
Prob. F(2,52)
Prob. Chi-Square(2)
0.0000
0.0000
P-value of Chi-square is 0.0000 < 0.01. Thus, we reject H0 and it is
believed that there is an autocorrelation problem.
87
4.3.2 Heteroscedasticity
Hypothesis:
H0: There is no heteroscedasticity problem.
H1: There is a heteroscedasticity problem.
Decision rules:
1.) We do not reject H0 if P-value of F-statistic> 0.01, which means there is
no heteroscedasticity problem.
2.) We reject H0 if P-value of F-statistic< 0.01, which means there is a
heteroscedasticity problem.
88
4.3.2.1 FTSE Bursa Malaysia (KLSE)
Table 14: Heteroskedasticity Test: Breusch-Pagan-Godfrey (KLSE)
F-statistic
Obs*R-squared
Scaled explained SS
3.133568
13.49361
12.50144
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
0.0148
0.0192
0.0285
P-value of F-statistic is 0.0148 > 0.01. Thus, we do not reject H0and
conclude that there is no heteroscedasticity problem.
4.3.2.2 The Stock Exchange of Thailand (SET)
Table 15: Heteroskedasticity Test: Breusch-Pagan-Godfrey (SET)
F-statistic
Obs*R-squared
Scaled explained SS
0.346221
1.863705
3.835686
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
0.8825
0.8677
0.5733
P-value of F-statistic is 0.8825 > 0.01. Thus, we do not reject H0 and
conclude that there is no heteroscedasticity problem.
89
4.3.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 16: Heteroskedasticity Test: Breusch-Pagan-Godfrey (IDX)
F-statistic
Obs*R-squared
Scaled explained SS
5.214872
19.53761
17.74562
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
0.0006
0.0015
0.0033
P-value of F-statistic is 0.0006 < 0.01. Thus, H0 is rejected and it is
believed that there is a heteroscedasticity problem.
4.3.2.4 The Philippine Stock Exchange (PSE)
Table 17: Heteroskedasticity Test: Breusch-Pagan-Godfrey (PSE)
F-statistic
Obs*R-squared
Scaled explained SS
2.175438
10.05949
14.97144
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
Conclusion:
P-value of F-statistic is 0.0704 > 0.01. Thus, we do not reject H0 and
conclude that there is no heteroscedasticity problem.
90
0.0704
0.0736
0.0105
4.3.3 Model Specification Test
Hypothesis:
H0: The model is properly specified.
H1: The model is not properly specified.
Decision rules:
1.) We do not reject H0 if P-value of F-statistic> 0.01, which meansthat the
model is correctly specified.
2.) We reject H0 if P-value of F-statistic< 0.01, which meansthat the model is
not correctly specified.
91
4.3.3.1 FTSE Bursa Malaysia (KLSE)
Table 18: RaPSEy RESET Test (KLSE)
t-statistic
F-statistic
Likelihood ratio
Value
0.721389
0.520401
0.586260
df
53
(1, 53)
1
Probability
0.4738
0.4738
0.4439
P-value of F-statistic is 0.4738 > 0.01. Thus, we do not reject H0 and
conclude that the model is properly specified.
4.3.3.2 The Stock Exchange of Thailand (SET)
Table 19: RaPSEy RESET Test (SET)
t-statistic
F-statistic
Likelihood ratio
Value
1.574592
2.479340
2.743128
df
53
(1, 53)
1
Probability
0.1213
0.1213
0.0977
P-value of F-statistic is 0.1213 > 0.01. Thus, we do not reject H0and
conclude that the model is properly specified.
92
4.3.3.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 20: RaPSEy RESET Test (IDX)
t-statistic
F-statistic
Likelihood ratio
Value
0.646221
0.417601
0.470903
df
53
(1, 53)
1
Probability
0.5209
0.5209
0.4926
P-value of F-statistic is 0.5209 > 0.01. Thus, we do not reject H0 and
conclude that the model is properly specified.
4.3.3.4 The Philippine Stock Exchange (PSE)
Table 21: RaPSEy RESET Test (PSE)
t-statistic
F-statistic
Likelihood ratio
Value
0.624134
0.389543
0.439380
df
53
(1, 53)
1
Probability
0.5352
0.5352
0.5074
P-value of F-statistic is 0.5352 > 0.01. Thus, we do not reject H0 and
conclude that the model is properly specified.
93
4.3.4 Normality Test
Hypothesis:
H0: Error term is normally distributed
H1: Error term is not normally distributed
Decision rules:
1.) We do not reject H0 if the P-value for JB-statistic is > 0.01, which means
the error term is normally distributed.
2.) We reject H0 if the P-value for JB-statistic is < 0.01, which means the
error term is not normally distributed.
94
4.3.4.1 FTSE Bursa Malaysia (KLSE)
Figure 3: Jarque-Bera Normality Test (KLSE)
10
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
8
6
4
2
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-1.13e-15
0.003144
0.228564
-0.165345
0.079103
0.196883
3.287584
Jarque-Bera
Probability
0.594391
0.742899
0
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
P-value of JB statistic is 0.742899 > 0.01. Thus, we do not reject H0
andsuggest that the error term is normally distributed.
95
4.3.4.2 The Stock Exchange of Thailand (SET)
Figure 4: Jarque-Bera Normality Test (SET)
12
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
10
8
6
4
2
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
5.29e-15
0.030121
0.260720
-0.643285
0.174679
-1.409465
6.081721
Jarque-Bera
Probability
43.60842
0.000000
0
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
P-value of JB statistic is 0.00000 < 0.01. Thus, we reject H0and it is
believed that error term is not normally distributed.
96
4.3.4.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Figure 5: Jarque-Bera Normality Test (IDX)
9
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
8
7
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
3.67e-15
0.017461
0.419050
-0.424652
0.170239
-0.314889
3.242667
Jarque-Bera
Probability
1.138766
0.565875
0
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
P-value of JB statistic is 0.565875 > 0.01. Thus, we do not reject
H0and conclude that the error term is normally distributed.
97
4.3.4.4 The Philippine Stock Exchange (PSE)
Figure 6: Jarque-Bera Normality Test (PSE)
9
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
8
7
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-2.83e-14
0.020140
0.221734
-0.414529
0.127955
-1.112923
4.674791
Jarque-Bera
Probability
19.39830
0.000061
0
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
P-value of JB statistic is 0.000061 < 0.01. Thus, we reject H0 and it is
believed that error term is not normally distributed.
98
4.3.5 F-stats
In this paper, F-test will be conductedtoexamine the significance ofthe
proposed economic model.
Hypothesis:
H0: βi = 0 (no linear relationship)
H1: βi ≠ 0 (at least one independent variable affects Y)
Where βi = β1, β2, … , βn
Decision rule:
We reject H0 if P-value of F-test is < 0.01 and conclude that at least one
independent variable is significant in explaining the dependent variable
(Gujarati and Porter, 2009).
Conclusion:
Table 6presents the Ordinary Least Square result of KLSEand the P-value of
F-test is 0.0000 < 0.01. Thus, we reject H0 and this concludes thatminimum
one independent variable is important in explaining and link with dependent
variable (KLSE).
The result of Ordinary Least Square result for SET is shown in Table 7and the
99
P-value of F-test is 0.0000 < 0.01. Thus, we reject H0and it is believed that
minimum one independent variable is important in explaining and has linkage
with dependent variable (SET).
According to Table 8, P-value of F-test is 0.0000 < 0.01, we reject H0. Thus,
we can conclude that at least one independent variable is important in
explaining the dependent variable (IDX).
Lastly, Table 9 shows that P-value of F-test is 0.0000 < 0.01, we reject H0.
Thus, we can conclude that at least one independent variable is important in
explaining the dependent variable (PSE).
4.4 Unit Root Test
Unit Root Test is used to analyze the stationary properties of the selected variables in
this paper. The purpose of performing this test is to examine the degree of stationarity
of thesevariables.
This paper employs Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) to test
for stationary with the corresponding variables. Results for both ADF and PP tests
will be presented in table format as per below.
100
4.4.1 FTSE Bursa Malaysia (KLSE)
Hypothesis:
H0: Log(KLSE) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is not
stationary and has a unit root.
H1: Log(KLSE) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is
stationary and do not contain unit root.
Table 22: Unit Root and Stationary Test Result (KLSE)
Test
Log(KLSE)
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
Log(KLSE)
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
ADF
Level
-0.921223
1.049576
-1.095165
0.776701
-1.766552
1.358552
First Difference
-4.361399*
-5.988379*
-6.038101*
-5.006029*
-4.646701*
-1.66804*
PP
-0.251454
1.438231
-1.198216
2.34293
-1.667351
3.46476
-4.176679*
-5.857968*
-6.031283*
-8.015529*
-4.638299*
-6.844980*
Note: * significant at 1%
Referring to Table 22, it shows that all variables are not significant at 1%, thus
it is believed that the variables in ADF and PP test are not stationary and have
unit root.Therefore, H0 is not rejected.
101
Next,results for First Difference show that all variables are significant at 1%
and this successfully rejects H0. Thisshows that all the variables are stationary
and do not contain unit root, which is supported by Gan et al. (2006).
4.4.2 The Stock Exchange of Thailand (SET)
Hypothesis:
H0: Log(SET) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is not
stationary and has a unit root.
H1: Log(SET) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is
stationary and do not contain unit root.
Table 23: Unit Root and Stationary Test Result (SET)
Test
Log(SET)
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
Log(SET)
Log(CPI)
Log(ER)
Log(GDP)
ADF
Level
-0.465000
0.473923
-1.091356
-0.822632
-3.049079
-0.444493
First Difference
-5.440427*
-7.242314*
-5.581983*
-9.921061*
102
PP
0.243883
0.472929
-0.808965
-0.688217
-2.465778
0.489031
-5.176594*
-7.900402*
-5.133133*
-11.27176*
Log(IR)
Log(M1)
-4.310726*
-2.503171*
-4.291386*
-8.138447*
Note: * significant at 1%
Table 23 presents the results of ADF and PP test, it shows that all the
variables are not significant at 1%, thus it is believed that the variables are not
stationary and have unit root. Therefore, H0 is not rejected.
Next, results for First Difference show that all variables are significant at 1%
and this rejects H0. This shows that all the variables are stationary and do not
contain unit root, which is supported by Gan et al. (2006).
4.4.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Hypothesis:
H0: Log(IDX) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is not
stationary and has a unit root.
H1: Log(IDX) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is
stationary and do not contain unit root.
Table 24: Unit Root and Stationary Test Result (IDX)
103
Test
Log(IDX)
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
Log(IDX)
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
ADF
Level
0.063547
0.580535
-1.768532
2.489739
-1.627181
2.609448
First Difference
-5.521440*
-7.593718*
-5.539726*
-1.987075
-3.906182*
-1.343207*
PP
0.538577
0.830883
-2.397494
7.786529
-1.864450
4.742781
-5.272251*
-7.676252*
-8.146429*
-9.866724*
-3.906182*
-7.692338*
Note: * significant at 1%
Table 23 shows the results and all the variables are not significant at 1%, thus
it is believed that the variables in ADF and PP test are not stationary and have
unit root. Therefore, H0 is not rejected.
Next, results for First Difference show that all variables are significant at 1%
and this rejects H0. This shows that all the variables are stationary and do not
contain unit root, which is supported by Gan et al. (2006).
4.4.4 The Philippine Stock Exchange (PSE)
104
Hypothesis:
H0: Log(PSE) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is not
stationary and has a unit root.
H1: Log(PSE) / Log(CPI) / Log(ER) / Log(GDP) / Log(IR) / Log(M1) is
stationary and do not contain unit root.
Table 25: Unit Root and Stationary Test Result (PSE)
Test
Log(PSE)
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
Log(PSE)
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
ADF
Level
0.578972
0.423897
-1.678442
3.287202
-0.791443
3.184925
First Difference
-5.421816*
-5.154024*
-5.586595*
-5.632279*
-6.515548*
-2.878024*
PP
1.526273
0.962931
3.131207
-0.849178
4.155296
-5.366121*
-4.994910*
-5.569601*
-5.677034*
-6.540864*
-5.019238*
Note: * significant at 1%
Referring to Table 25, results show that all the variables are not significant at
1%, thus it is believed that the variables in ADF and PP test are not stationary
and have unit root. Therefore, H0 is not rejected.
Next, results for First Difference show that all variables are significant at 1%
105
and this rejects H0. This shows that all the variables are stationary and do not
contain unit root, which is supported by Gan et al. (2006).
4.5 Johansen Cointegration Test
After setting up the order of integration for these variables, next step is to examine the
cointegration properties of the data series. Johansen & Juselius Cointegration test is
used to examine the existence of long run equilibrium relationship in the linear
combination of the series.
Hypothesis:
H0: Long-run relationship does not exist between these variables.
H1: Long-run relationship exists between these variables.
4.5.1 FTSE Bursa Malaysia (KLSE)
Table 26: Johansen-Juselius Cointegration Tests (KLCI)
Test statistic
106
H0
Trace
5%
r=0
r=1
r=2
r=3
r=4
r=5
98.25244*
63.78095
36.87925
17.25646
2.735725
0.244654
95.75366
69.81889
47.85613
29.79707
15.49471
3.841466
Maximum
Eigenvalue
34.47149
26.90170
19.62280
14.52073
2.491071
0.244654
5%
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
Notes: * rejection of the hypothesis at the 5% significant level.
Cointegration Test result is shown in Table 26 and itshows that at least one
(r=0) cointegration is significant at 5% and hence rejectH0. This concludes
that there is long run relationship between variables for KLSE.
4.5.2 The Stock Exchange of Thailand (SET)
Table27: Johansen-Juselius Cointegration Tests (SET)
H0
Trace
Test statistic
Maximum
5%
Eigenvalue
95.75366 47.65535*
69.81889
28.21912
47.85613
15.84965
29.79707
5.683810
15.49471
3.622715
3.841466
2.132389
5%
r=0 103.1630*
40.07757
r=1 55.50768
33.87687
r=2 27.28856
27.58434
r=3 11.43891
21.13162
r=4 5.755104
14.26460
r=5 2.132389
3.841466
Notes: * rejection of the hypothesis at the 5% significant level.
107
Cointegration Test result is shown in Table 27 and it shows that at least one
(r=0) cointegration is significant at 5% and hence reject H0. This concludes
that there is long run relationship between variables for SET.
4.5.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 28: Johansen-Juselius Cointegration Tests (IDX)
H0
Trace
r=0
r=1
r=2
r=3
r=4
r=5
112.0398*
68.92330
37.11932
22.06705
10.81013
1.999736
Test statistic
Maximum
5%
Eigenvalue
95.75366 43.11653*
69.81889
31.80398
47.85613
15.05227
29.79707
11.25692
15.49471
8.810393
3.841466
1.999736
5%
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
Notes: * rejection of the hypothesis at the 5% significant level.
Cointegration Test result is shown in Table 28 and it shows that at least one
(r=0) cointegration is significant at 5% and hence reject H0. This concludes
that there is long run relationship between variables for IDX.
108
4.5.4 The Philippine Stock Exchange (PSE)
Table 29: Johansen-Juselius Cointegration Tests (PSE)
H0
Trace
r=0
r=1
r=2
r=3
r=4
r=5
79.98270
50.83728
27.42157
12.90186
3.379081
0.275226
Test statistic
Maximum
5%
Eigenvalue
95.75366
29.14542
69.81889
23.41571
47.85613
14.51971
29.79707
9.522782
15.49471
3.103856
3.841466
0.275226
5%
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
Notes: * rejection of the hypothesis at the 5% significant level.
Cointegration Test result is shown in Table 28. From the result, Trace
and Maximum Eigenvalue indicate that no cointegration is significant
at 5% and hence H0 is not rejected. Therefore, it is believed that there
is no long run relationship between variables for PSE.
4.6 Granger Causality Test
This paper applies Granger Causality test that introduced by Sir Clive William John
Granger. He claimed that variables can be used to predict each other if there is causal
relationship exist betweenthem (Ali et al., 2010).
109
4.6.1 FTSE Bursa Malaysia (KLSE)
Table 30: Short- term Granger Causality Tests E-view Output (KLSE)
VAR Granger Causality/Block Exogeneity Wald Tests
Date: 08/09/15 Time: 02:23
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: KLSE
Excluded
Chi-sq
df
Prob.
CPI
ER
GDP
IR
M1
10.24580
1.091228
3.573267
1.606198
9.043812
2
2
2
2
2
0.0060
0.5795
0.1675
0.4479
0.0109
All
37.87183
10
0.0000
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
110
Table 31: Short- term Granger Causality Tests Result (KLSE)
Dependent Variable: LKLSE
Independent Variable
LCPI (Consumer Price Index)
LER (Exchange Rate)
LGDP (Gross Domestic Product)
LIR (Interest Rate)
LM1 (Money Supply)
P-Value
Result
0.0060***
0.5795
0.1675
0.4479
0.0109**
Significant
Insignificant
Insignificant
Insignificant
Significant
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Table 32: Summary of Short-term Granger Causality Tests Results between
all variables (KLSE)
Variables
Log(KLSE) Log(CPI) Log(ER) Log(GDP) Log(IR) Log(M1)
1%
5%
Log(KLSE)
Log(CPI)
1%
1%
1%
Log(ER)
1%
1%
1%
Log(GDP)
10%
5%
5%
Log(IR)
Log(M1)
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
111
Figure 7: The relationship between each variables for Granger Causality Tests
(KLSE)
Hypothesis:
Hypothesis 1
H0: There is no relationship between stock market returns of FTSE Bursa
Malaysia (KLSE) and Consumer Price Index (CPI).
H1: There is a relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and Consumer Price Index (CPI).
Result in Table 32 shows that KLCI is affected by CPI. This is due to P-value
112
of CPI (0.006) is significant at 1% and this says that CPI has Granger cause
impact on KLSE. Thus, this study will reject H0 and conclude that there is
relationship between stock market returns of KLSEand Consumer Price Index.
Hypothesis 2
H0: There is no relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and exchange rate (ER).
H1: There is a relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and exchange rate (ER).
Table 32 shows that KLSEis not affected by ER. This is due to P-value of
LCPI (0.5795) is not significant and this also means that ER do not has
Granger cause impact on KLSE. Thus, this study will not reject H0 and there
is no Granger cause relationship between stock market returns of KLSEand
exchange rate in short run.
Hypothesis 3
H0: There is no relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and Gross Domestic Product (GDP).
H1: There is a relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and Gross Domestic Product (GDP).
113
The test shows result P-value of GDP (0.1675) is not significant at 10%. Thus,
this study does not reject H0 and conclude that there is no Granger cause
relationship between GDP and KLCI in short run.
Hypothesis 4
H0: There is no relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and interest rate (IR).
H1: There is a relationship between stock market returns of FTSE Bursa
Malaysia (KLSE) and interest rate (IR).
Table 32 shows that KLSEis not affected by IR. This is due to P-value of IR
(0.4479) is not significant and this also means that IR does not have Granger
cause impact on KLSE. Thus, this study will not reject H0and there is no
relationship between stock market returns of KLSEand interest rate in short
run.
Hypothesis 5
H0: There is no relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and money supply (M1).
H1: There is a relationship between the stock market returns of FTSE Bursa
Malaysia (KLSE) and money supply (M1).
Result in Table 32 shows that P-value of M1 (0.0109) is significant at 5%.
114
Thus, this study will reject H0 and this indicates that there is short term
relationship between stock market returns of KLSEand money supply.
4.6.2 The Stock Exchange of Thailand (SET)
Table 33: Short- term Granger Causality Tests E-view Output (SET)
VAR Granger Causality/Block Exogeneity Wald Tests
Date: 08/09/15 Time: 02:23
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: SET
Excluded
Chi-sq
df
Prob.
CPI
ER
GDP
IR
M1
8.110543
2.294569
5.557788
8.884119
18.19071
2
2
2
2
2
0.0173
0.3175
0.0621
0.0118
0.0001
All
40.49919
10
0.0000
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Table 34: Short- term Granger Causality Tests Result (SET)
Dependent Variable: LSET
Independent Variable
LCPI (Consumer Price Index)
LER (Exchange Rate)
115
P-Value
Result
0.0173**
0.3175
Significant
Insignificant
LGDP (Gross Domestic Product)
LIR (Interest Rate)
LM1 (Money Supply)
0.0621***
0.0118**
0.0001*
Significant
Significant
Significant
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Table 35: Summary of Short-term Granger Causality Tests Results between
all variables (SET)
Variables Log(SET) Log(CPI) Log(ER) Log(GDP) Log(IR) Log(M1)
5%
10%
5%
1%
Log(SET)
5%
1%
Log(CPI)
5%
1%
Log(ER)
1%
1%
5%
1%
5%
Log(GDP)
5%
1%
5%
Log(IR)
1%
1%
Log(M1)
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Figure 8: The relationship between each variables for Granger Causality Tests
(SET)
116
Hypothesis 1
H0: There is no relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and Consumer Price Index (CPI).
H1: There is a relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and Consumer Price Index (CPI).
Result in Table 35 shows that SET is affected by CPI. This is due to P-value
ofCPI (0.0173) is significant at 5% and this indicates that CPI has Granger
cause impact on SET. Thus, this study will reject H0 and there is relationship
between stock market returns of SET and CPI in short run.
Hypothesis 2
H0: There is no relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and exchange rate (ER).
H1: There is a relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and exchange rate (ER).
Table 35shows that SET is not affected by ER. This is due to P-value of ER
(0.3175) is not significant and this also means that ER does not has Granger
cause impact on SET. Thus, this study will not reject H0 and there is no
relationship between stock market returns of SET and exchange rate.
However, there is bilateral relationship between ER and GDP, which might
indirectly cause SET to move.
117
Hypothesis 3
H0: There is no relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and Gross Domestic Product (GDP).
H1: There is a relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and Gross Domestic Product (GDP).
The test shows result P-value of GDP (0.0621) is significant at 10%
significant level. Thus, H0 will be rejected and this indicates that there is
strong Granger cause relationship between GDP and SET in short run.
Hypothesis 4
H0: There is no relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and interest rate (IR).
H1: There is a relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and interest rate (IR).
Table 35 shows that SET is affected by IR. This is due to P-value of IR
(0.0118) is significant at 5% significance level and this shows that IR has
Granger cause impact on SET. Thus, this study will reject H0 and concludes
that there is relationship between stock market returns of SET and interest
rate. Besides, there is also bilateral relationship between interest rate (IR) and
118
money supply (M1).
Hypothesis 5
H0: There is no relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and money supply (M1).
H1: There is a relationship between the stock market returns of The Stock
Exchange of Thailand (SET) and money supply (M1).
Result in Table 35 shows that P-value of M1 (0.0001) is significant at 10%
significant level. Hence, this study will reject H0 and there is relationship
between stock return of emerging countries in SET and money supply. In
short, it can be said that money supply will affect the stock return of SET. In
addition, money supply has bilateral relationship with consumer price index
and interest rate.
4.6.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 36: Short- term Granger Causality Tests E-view Output (IDX)
VAR Granger Causality/Block Exogeneity Wald Tests
Date: 08/09/15 Time: 02:23
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: IDX
119
Excluded
Chi-sq
df
Prob.
CPI
ER
GDP
IR
M1
3.372109
0.829057
8.356517
3.361397
4.359590
2
2
2
2
2
0.1852
0.6607
0.0153
0.1862
0.1131
All
26.29147
10
0.0034
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Table 37: Short- term Granger Causality Tests Result (IDX)
Dependent Variable: LIDX
Independent Variable
LCPI (Consumer Price Index)
LER (Exchange Rate)
LGDP (Gross Domestic Product)
LIR (Interest Rate)
LM1 (Money Supply)
P-Value
Result
0.1852
0.6607
0.0153**
0.1862
0.1131
Insignificant
Insignificant
Significant
Insignificant
Insignificant
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Table 38: Summary of Short-term Granger Causality Tests Results between
all variables (IDX)
Variables Log(IDX) Log(CPI) Log(ER)
Log(IDX)
5%
Log(CPI)
Log(ER)
1%
Log(GDP)
5%
Log(IR)
10%
Log(M1)
120
Log(GDP) Log(IR)
5%
5%
1%
-
Log(M1)
1%
-
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Figure 9: The relationship between each variables for Granger Causality Tests
((IDX)
Hypothesis 1
H0: There is no relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and Consumer Price Index (CPI).
121
H1: There is a relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and Consumer Price Index (CPI).
Result in Table 38 shows that IDX will not be affected by CPI. This is due to
P-value of LCPI (0.1852) is not significant at 10% significant level and this
also means that CPI does not have Granger cause impact on IDX in short run.
Thus, this study will not reject H0 and there is no relationship between stock
market returns of IDX and CPI.
Hypothesis 2
H0: There is no relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and exchange rate (ER).
H1: There is a relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and exchange rate (ER).
Table 38 shows that IDX is not affected by ER. This is due to P-value of ER
(0.6607) is not significant and this also means that ER does not has Granger
cause impact on IDX. Thus, this study will not reject H0 and this shows that
there is no relationship between stock market returns of IDX and exchange
rate.
Hypothesis 3
122
H0: There is no relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and Gross Domestic Product (GDP).
H1: There is a relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and Gross Domestic Product (GDP).
The test shows result P-value of GDP (0.0153) is significant at 5% significant
level. Thus, H0 will be rejected and this shows that there is strong Granger
cause relationship between GDP and IDX in short run. Besides, there is also
bilateral relationship between GDP and money supply (M1).
Hypothesis 4
H0: There is no relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and interest rate (IR).
H1: There is a relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and interest rate (IR).
Table 38 shows that IDX is not affected by IR. This is due to P-value of IR
(0.1862) is not significant at 10% and this shows that IR does not has a
Granger cause impact on IDX. Thus, this study will not reject H0and there is
no short term relationship between stock market returns of IDX and interest
rate.
123
Hypothesis 5
H0: There is no relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and money supply (M1).
H1: There is a relationship between stock market returns of Indonesia Stock
Exchange (Bursa Efek Indonesia, IDX) and money supply (M1).
Result in Table 38 shows that P-value of LM1 (0.1131) is not significant at
10% significant level. Thus, this study will not reject H0 and this indicates
that there is no relationship between stock market returns of IDX and money
supply. In short, money supply will not affect the stock return of IDX.
However, money supply has bilateral relationship with Gross Domestic
Product (GDP), which might cause indirect impact on IDX.
4.6.4 The Philippine Stock Exchange (PSE)
Table 39: Short- term Granger Causality Tests E-view Output (PSE)
VAR Granger Causality/Block Exogeneity Wald Tests
Date: 08/09/15 Time: 02:23
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: PSE
Excluded
Chi-sq
df
124
Prob.
CPI
ER
GDP
IR
M1
3.271029
0.611073
0.054273
0.151705
1.942906
2
2
2
2
2
0.1949
0.7367
0.9732
0.9270
0.3785
All
12.32087
10
0.2642
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Table 40: Short- term Granger Causality Tests Result (PSE)
Dependent Variable: LPSE
Independent Variable
LCPI (Consumer Price Index)
LER (Exchange Rate)
LGDP (Gross Domestic Product)
LIR (Interest Rate)
LM1 (Money Supply)
P-Value
0.1949
0.7367
0.9732
0.9270
0.3785
Result
Insignificant
Insignificant
Insignificant
Insignificant
Insignificant
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
Table 41: Summary of Short-term Granger Causality Tests Results between
all variables (PSE)
Variables Log(PSE) Log(CPI) Log(ER)
Log(PSE)
10%
Log(CPI)
5%
Log(ER)
1%
5%
Log(GDP)
10%
10%
Log(IR)
Log(M1)
Note:
*** Significant at 1% significance level
** Significant at 5% significance level
* Significant at 10% significance level
125
Log(GDP) Log(IR)
10%
10%
-
Log(M1)
-
Figure 10: The relationship between each variables for Granger Causality
Tests (PSE)
Hypothesis 1
126
H0: There is no relationship between stock market returns of The Philippine
Stock Exchange (PSE) and Consumer Price Index (CPI).
H1: There is a relationship between stock market returns of The Philippine
Stock Exchange (PSE) and Consumer Price Index (CPI).
Result in Table 41 shows that PSE will not be affected by CPI. This is due to
P-value of CPI (0.1949) is not significant at 10% significant level and this also
means that CPI does not have Granger cause impact on PSE. Thus, this study
will not reject H0 and this shows that there is no short term relationship
between stock return of PSE and CPI. However, CPI has bilateral relationship
with exchange rate (ER) and Gross Domestic Product (GDP).
Hypothesis 2
H0: There is no relationship between stock market returns of The Philippine
Stock Exchange (PSE) and exchange rate (ER).
H1: There is a relationship between stock market returns of The Philippine
Stock Exchange (PSE) and exchange rate (ER).
Table 41 shows that PSE is not affected by ER. This is due to P-value of LCPI
(0.7367) is not significant and this also means that ER does not has Granger
cause impact on PSE. Thus, this study will not reject H0and there is no
relationship between stock return of PSE and exchange rate. In addition, there
is bilateral relationship between Consumer Price Index (CPI) and exchange
rate (ER).
Hypothesis 3
H0: There is no relationship between stock market returns of The Philippine
127
Stock Exchange (PSE) and Gross Domestic Product (GDP).
H1: There is a relationship between stock market returns of The Philippine
Stock Exchange (PSE) and Gross Domestic Product (GDP).
The test shows result P-value of GDP (0.9732) is not significant at 10%
significant level. Thus, H0 will not be rejected and there is no Granger cause
relationship between Gross Domestic Product and PSE in short run. However,
GDP has a bilateral relationship with Consumer Price Index (CPI).
Hypothesis 4
H0: There is no relationship between stock market returns of The Philippine
Stock Exchange (PSE) and interest rate (IR).
H1: There is a relationship between stock market returns of The Philippine
Stock Exchange (PSE) and interest rate (IR).
Table 41 shows that PSE is not affected by IR. This is due to P-value of IR
(0.9270) is not significant at 10% significance level and this also means that
IR does not has Granger cause impact on PSE. Thus, this study will not reject
H0 and there is no relationship between stock return of PSE and interest rate
in short run.
Hypothesis 5
H0: There is no relationship between stock market returns of The Philippine
Stock Exchange (PSE) and money supply (M1).
H1: There is a relationship between stock market returns of The Philippine
Stock Exchange (PSE) and money supply (M1).
128
Result in Table 41 shows that P-value of M1 (0.3758) is not significant at
10% significant level. Thus, this study will not reject H0 and there is no short
term relationship between stock return of PSE and money supply.
4.7 Variance Decomposition
Variance decomposition determines the amount of information that
contributed by each variables to one another in an auto-regression. It verifies
how much of the forecast error variance of each variable can be explained by
exogenous shocks to other variables (Brooks, 2008).
4.7.1 FTSE Bursa Malaysia (KLSE)
Table 42: Variance Decomposition of Log(KLSE) towards Log(CPI),
Log(ER), Log(GDP), Log(IR), Log(M1)
Perio
d
S.E.
CPI
ER
GDP
IR
M1
KLCI
1
0.624899
0.000000
0.000000
0.000000
0.000000
0.000000
100.0000
2
0.897602
4.442400
0.236227
0.249426
0.840920
4.539218
89.69181
3
1.053333
7.762350
1.251321
0.700411
1.268518
5.661452
83.35595
4
1.206485
7.756893
1.754412
0.802556
1.886315
6.342476
81.45735
5
1.375096
7.306911
1.777736
0.802721
2.268323
6.894088
80.95022
6
1.545235
7.659336
1.747459
0.834958
2.312430
7.168248
80.27757
129
7
1.700269
8.770503
1.745665
0.910983
2.289942
7.146299
79.13661
8
1.830135
10.27163
1.759649
0.984122
2.374731
7.010168
77.59971
9
1.936809
11.59214
1.776312
1.035196
2.559191
6.869891
76.16727
10
2.029342
12.33614
1.767028
1.078617
2.763331
6.733055
75.32183
Hypothesis:
H0: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) do not have an impact
on stock return of FTSE Bursa Malaysia (KLSE).
H1: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) have impact on stock
return of FTSE Bursa Malaysia (KLSE).
From the result above, it shows that shock in exchange rate has a smaller
impact of 0.236227 percent in KLSEin period 2 (short run). However,
Consumer Price Index (CPI) has a larger impact of 12.33614 percent on
KLSEin period 10 (long run).
On the other hand, from the data, it indicates that the shock on independent
variable to dependent variable gets greater as it risesgradually from Period 1
to Period 10.
Generally, it is believed that the impacts of independent variable in short run
is minimal and can see larger impact in the long run on dependent variable.
Therefore, this paper rejectsH0and concludes that the selected macroeconomic
variables have impacts on stock market returns of FTSE Bursa Malaysia
(KLSE).
130
4.7.2 The Stock Exchange of Thailand (SET)
Table 43: Variance Decomposition of Log(SET) towards Log(CPI), Log(ER),
Log(GDP), Log(IR), Log(M1)
Perio
d
S.E.
CPI
ER
GDP
IR
M1
SET
1
0.785125
0.000000
0.000000
0.000000
0.000000
0.000000
100.0000
2
1.225581
2.272917
0.324451
5.844149
0.665269
2.193643
88.69957
3
1.534560
9.951370
0.808555
7.682945
0.980105
8.711119
71.86591
4
1.761004
17.17116
0.662929
6.724602
1.136182
13.21128
61.09385
5
1.946082
19.96773
1.715851
6.196467
1.484035
15.06870
55.56721
6
2.109496
20.15923
3.368694
6.683399
1.934361
16.35446
51.49985
7
2.251283
19.82437
4.295741
7.597037
2.209048
18.21586
47.85794
8
2.371633
19.58966
4.607700
8.630860
2.284690
20.34314
44.54396
9
2.479313
19.40275
4.697836
9.661923
2.303651
22.08638
41.84746
10
2.583648
19.14887
4.677638
10.51870
2.345160
23.48500
39.82463
Hypothesis:
H0: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) do not have an impact
on stock return of The Stock Exchange of Thailand (SET).
H1: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) has an impact on
stock return of The Stock Exchange of Thailand (SET).
From result, t shows that shock in exchange rate has a smaller impact of
0.324451 percent in SET in period 2 (short run). However, money supply
(M1) has a larger impact of 23.48500 percent on SET in period 10 (long run).
On the other hand, from the data, it indicates that the impacts on independent
131
variable to dependent variable getlarger as it rises gradually from Period 1 to
Period 10.
Generally, it is believed that the impacts of independent variable in short run
is minimal and can see larger impact in the long run on dependent variable.
Therefore, this paper rejects H0 and concludes that the selected
macroeconomic variables have impacts on stock market returns of The Stock
Exchange of Thailand (SET).
4.7.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 44: Variance Decomposition of Log(IDX) towards Log(CPI), Log(ER),
Log(GDP), Log(IR), Log(M1)
Perio
d
S.E.
CPI
ER
GDP
IR
M1
IDX
1
0.943006
0.000000
0.000000
0.000000
0.000000
0.000000
100.0000
2
1.234363
1.383350
0.652153
1.977485
0.870425
0.068381
95.04821
3
1.409893
1.979865
1.388787
11.47368
1.724159
0.869101
82.56440
4
1.566303
1.752370
1.592533
18.15365
2.259125
0.748195
75.49413
5
1.727891
1.675532
1.811095
19.91139
2.303263
1.002248
73.29647
6
1.887340
1.865358
2.074036
20.30410
2.254157
1.596519
71.90583
7
2.032440
2.272380
2.389638
20.41939
2.393773
2.179172
70.34565
8
2.158012
2.700790
2.809554
20.36613
2.610999
2.697928
68.81460
9
2.264434
2.956879
3.414328
20.23455
2.721945
3.091607
67.58069
132
10
2.356305
3.021906
4.180969
20.12561
2.691737
3.335199
66.64458
Hypothesis:
H0: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) do not have an impact
on stock return of Indonesia Stock Exchange (Bursa Efek Indonesia, IDX).
H1: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) has an impact on
stock return of Indonesia Stock Exchange (Bursa Efek Indonesia, IDX).
From the data above, the result shows that shock in money supply (M1) has a
smaller impact of 0.0683481 percent in IDX in period 2 (short run). However,
Gross Domestic Product (GDP) has a larger impact of 20.12561 percent on
IDX in period 10 (long run).
On the other hand, from the data, it indicates that the impact on independent
variable to dependent variable is getting greater as it increases gradually.
Generally, it is believed that the impact of independent variable in short run is
minimal and can see larger impact in the long run on dependent variable.
Therefore, this paper rejects H0 and concludes that the selected
macroeconomic variables have impacts on stock market returns of Indonesia
Stock Exchange (Bursa Efek Indonesia, IDX).
4.7.4 The Philippine Stock Exchange (PSE)
Table 45: Variance Decomposition of Log(PSE) towards Log(CPI), Log(ER),
Log(GDP), Log(IR), Log(M1)
Perio
S.E.
CPI
ER
GDP
133
IR
M1
PSE
d
1
2
3
4
5
6
7
8
9
10
0.683857
1.097985
1.410066
1.683030
1.939998
2.188009
2.429404
2.664078
2.890470
3.106860
0.000000
1.967344
3.333321
4.059460
4.689291
5.386441
6.131203
6.870522
7.571759
8.221094
0.000000
0.043433
0.465321
1.333894
2.510198
3.808583
5.080606
6.228821
7.206604
8.009596
0.000000
0.003171
0.003487
0.062267
0.264780
0.544328
0.788029
0.944825
1.018071
1.032715
0.000000
0.352930
0.469256
0.409415
0.364747
0.391699
0.479326
0.605214
0.750842
0.904375
0.000000
0.893164
1.365036
1.199178
1.100617
1.401786
2.139467
3.213480
4.464039
5.736653
100.0000
96.73996
94.36358
92.93579
91.07037
88.46716
85.38137
82.13714
78.98868
76.09557
Hypothesis:
H0: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) do not have an impact
on stock return of The Philippine Stock Exchange (PSE).
H1: Log(CPI), Log(ER), Log(GDP), Log(IR), Log(M1) has an impact on
stock return of The Philippine Stock Exchange (PSE).
From the data above, the result shows that shock in money supply (M1) has a
smaller impact of 0.003171 percent in PSE in period 2 (short run). However,
Consumer Price Index (CPI) has a larger impact of 8.221094 percent on PSE
in period 10 (long run).
On the contrary, from the data, it indicates that the impact on independent
variable to dependent variable gets greater as it rises gradually from Period 1
to Period 10.
Generally, it is believed that the shocks of independent variable in short run is
minimal and can see larger impact in the long run on dependent variable.
Therefore, this paper rejects H0 and concludes that the selected
macroeconomic variables have impacts on stock market returns of The
Philippine Stock Exchange (PSE).
134
4.8 Impulse Response Function (IRF)
Generally, Impulse Response Function (IRF) gives responses when the system is
shocked by a one-standard-deviation shock. In other words, an impulse response
refers to the reaction of the system as a function of time.
4.8.1 FTSE Bursa Malaysia (KLSE)
Figure 11: Impulse Response Function of Log(KLSE) to Shocks in System
Macroeconomic Variables
135
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of KLCI to KLCI
Response of KLCI to CPI
Response of KLCI to ER
Response of KLCI to GDP
Response of KLCI to IR
Response of KLCI to M1
120
120
120
120
120
120
80
80
80
80
80
80
40
40
40
40
40
40
0
0
0
0
0
-40
-40
1
2
3
4
5
6
7
8
9
10
-40
1
2
Response of CPI to KLCI
3
4
5
6
7
8
9
10
-40
1
2
Response of CPI to CPI
3
4
5
6
7
8
9
10
0
-40
1
2
Response of CPI to ER
3
4
5
6
7
8
9
10
-40
1
2
Response of CPI to GDP
3
4
5
6
7
8
9
10
1
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
1
2
3
4
5
6
7
8
9
10
1
2
Response of ER to KLCI
3
4
5
6
7
8
9
10
1
2
Response of ER to CPI
.05
3
4
5
6
7
8
9
10
1
2
Response of ER to ER
.10
.05
3
4
5
6
7
8
9
10
1
2
Response of ER to GDP
.10
.05
3
4
5
6
7
8
9
10
1
.05
.05
.00
.00
.00
.00
-.05
-.05
-.05
-.05
-.10
3
4
5
6
7
8
9
10
-.10
1
2
Response of GDP to KLCI
3
4
5
6
7
8
9
10
-.10
1
2
Response of GDP to CPI
8,000
8,000
4,000
3
4
5
6
7
8
9
10
-.10
1
2
Response of GDP to ER
4
5
6
7
8
9
10
2
4,000
3
4
5
6
7
8
9
10
1
4,000
4,000
0
0
0
0
-4,000
-4,000
-4,000
-4,000
-8,000
3
4
5
6
7
8
9
10
-8,000
1
2
Response of IR to KLCI
3
4
5
6
7
8
9
10
-8,000
1
2
Response of IR to CPI
3
4
5
6
7
8
9
10
-8,000
1
2
Response of IR to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
.1
.1
.1
.0
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.1
-.1
3
4
5
6
7
8
9
10
1
2
Response of M1 to KLCI
3
4
5
6
7
8
9
10
1
2
Response of M1 to CPI
4,000
3
4
5
6
7
8
9
10
1
2
Response of M1 to ER
8,000
4,000
3
4
5
6
7
8
9
10
1
2
4,000
3
4
5
6
7
8
9
10
1
4,000
4,000
0
0
0
0
-4,000
-4,000
-4,000
-4,000
-8,000
-8,000
-8,000
-8,000
-8,000
-8,000
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
4,000
0
-4,000
3
7
8,000
0
2
6
Response of M1 to M1
8,000
-4,000
1
2
Response of M1 to IR
8,000
5
.1
Response of M1 to GDP
8,000
4
Response of IR to M1
.1
2
2
Response of IR to IR
.0
1
3
-8,000
1
Response of IR to GDP
.1
8,000
10
4,000
0
-4,000
2
9
8,000
0
1
8
Response of GDP to M1
8,000
-4,000
-8,000
2
Response of GDP to IR
8,000
7
-.10
1
Response of GDP to GDP
8,000
4,000
3
6
.05
.00
-.05
2
5
.10
.00
1
4
Response of ER to M1
.10
-.05
-.10
2
Response of ER to IR
.10
3
Response of CPI to M1
1.0
.10
2
Response of CPI to IR
1.0
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
Figure 11 shows the result of IRF of the selected macroeconomic variables on
KLSE. KLSEreacts to its own innovations but the effect is reducinggradually.
Also, the innovations to CPI, ER and IR always have a negative shock on
KLSE. Lastly, it indicates that GDP and M1 have a positive shock on stock
market.
4.8.2 The Stock Exchange of Thailand (SET)
Figure 12: Impulse Response Function of Log(SET) to Shocks in System
Macroeconomic Variables
136
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of BSE to BSE
Response of BSE to CPI
Response of BSE to ER
Response of BSE to GDP
Response of BSE to IR
Response of BSE to M1
80
80
80
80
80
80
40
40
40
40
40
40
0
0
0
0
0
0
-40
-40
-40
-40
-40
-40
-80
-80
1
2
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to BSE
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to CPI
1.0
1.0
0.5
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to ER
1.0
0.5
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to GDP
1.0
0.5
3
4
5
6
7
8
9
10
1
0.5
0.5
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
Response of ER to CPI
3
4
5
6
7
8
9
10
1
2
Response of ER to ER
3
4
5
6
7
8
9
10
1
2
Response of ER to GDP
3
4
5
6
7
8
9
10
1
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-1.0
2
3
4
5
6
7
8
9
10
-1.0
1
2
Response of GDP to BSE
3
4
5
6
7
8
9
10
-1.0
1
2
Response of GDP to CPI
3
4
5
6
7
8
9
10
-1.0
1
2
Response of GDP to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
30,000
30,000
30,000
20,000
20,000
20,000
20,000
20,000
20,000
10,000
10,000
10,000
10,000
10,000
0
0
0
0
0
0
-10,000
-10,000
-10,000
-10,000
-10,000
-10,000
-20,000
-20,000
-20,000
-20,000
-20,000
-20,000
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
Response of IR to CPI
3
4
5
6
7
8
9
10
1
2
Response of IR to ER
3
4
5
6
7
8
9
10
1
2
Response of IR to GDP
3
4
5
6
7
8
9
10
1
.3
.3
.3
.2
.2
.2
.2
.2
.2
.1
.1
.1
.1
.1
.0
.0
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.1
-.1
-.2
-.2
-.2
-.2
-.2
-.2
3
4
5
6
7
8
9
10
1
2
Response of M1 to BSE
3
4
5
6
7
8
9
10
1
2
Response of M1 to CPI
20
3
4
5
6
7
8
9
10
1
2
Response of M1 to ER
40
20
3
4
5
6
7
8
9
10
1
2
20
3
4
5
6
7
8
9
10
1
20
20
0
0
0
0
-20
-20
-20
-20
-40
-40
-40
-40
-40
-40
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
20
0
-20
3
5
40
0
2
4
Response of M1 to M1
40
-20
1
2
Response of M1 to IR
40
3
.1
Response of M1 to GDP
40
10
Response of IR to M1
.3
2
2
Response of IR to IR
.3
1
9
10,000
.3
40
8
Response of GDP to M1
30,000
Response of IR to BSE
2
Response of GDP to IR
30,000
2
7
-1.0
1
Response of GDP to GDP
30,000
1
6
Response of ER to M1
1.0
1
2
Response of ER to IR
1.0
-1.0
5
0.5
0.0
-0.5
Response of ER to BSE
4
1.0
0.0
2
3
Response of CPI to M1
1.0
-0.5
1
2
Response of CPI to IR
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
Figure 12 shows the result of IRF of the selected macroeconomic variables on
SET. From the result, SETreacts to its own innovations but the effect is
reducingregularly. Also, result shows innovations to CPI and IRhave negative
shocks on SET while ER,GDP and M1 have positive shocks on stock market
returns of SET.
4.8.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Figure 13: Impulse Response Function of Log(IDX) to Shocks in System
Macroeconomic Variables
137
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of IDX to IDX
Response of IDX to CPI
Response of IDX to ER
Response of IDX to GDP
Response of IDX to IR
Response of IDX to M1
300
300
300
300
300
300
200
200
200
200
200
200
100
100
100
100
100
100
0
0
0
0
0
-100
-100
1
2
3
4
5
6
7
8
9
10
-100
1
2
Response of CPI to IDX
3
4
5
6
7
8
9
10
-100
1
2
Response of CPI to CPI
3
4
5
6
7
8
9
10
0
-100
1
2
Response of CPI to ER
3
4
5
6
7
8
9
10
-100
1
2
Response of CPI to GDP
3
4
5
6
7
8
9
10
1
1.0
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
2
3
4
5
6
7
8
9
10
1
2
Response of ER to IDX
3
4
5
6
7
8
9
10
1
2
Response of ER to CPI
3
4
5
6
7
8
9
10
1
2
Response of ER to ER
3
4
5
6
7
8
9
10
1
2
Response of ER to GDP
3
4
5
6
7
8
9
10
1
800
800
800
800
400
400
400
400
400
400
0
0
0
0
0
0
-400
-400
-400
-400
-400
-400
-800
2
3
4
5
6
7
8
9
10
-800
1
2
Response of GDP to IDX
3
4
5
6
7
8
9
10
-800
1
2
Response of GDP to CPI
3
4
5
6
7
8
9
10
-800
1
2
Response of GDP to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
12,000
12,000
12,000
8,000
8,000
8,000
8,000
8,000
8,000
4,000
4,000
4,000
4,000
4,000
4,000
0
0
0
0
0
-4,000
3
4
5
6
7
8
9
10
-4,000
1
2
Response of IR to IDX
3
4
5
6
7
8
9
10
-4,000
1
2
Response of IR to CPI
3
4
5
6
7
8
9
10
2
Response of IR to ER
3
4
5
6
7
8
9
10
2
Response of IR to GDP
3
4
5
6
7
8
9
10
1
.8
.8
.8
.4
.4
.4
.4
.4
.4
.0
.0
.0
.0
.0
.0
-.4
-.4
-.4
-.4
-.4
-.4
-.8
3
4
5
6
7
8
9
10
-.8
1
2
Response of M1 to IDX
3
4
5
6
7
8
9
10
-.8
1
2
Response of M1 to CPI
3
4
5
6
7
8
9
10
-.8
1
2
Response of M1 to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
30,000
30,000
30,000
20,000
20,000
20,000
20,000
20,000
20,000
10,000
10,000
10,000
10,000
10,000
10,000
0
0
0
0
0
-10,000
3
4
5
6
7
8
9
10
-10,000
1
2
3
4
5
6
7
8
9
10
-10,000
1
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
4
5
6
3
4
5
6
3
4
5
6
3
4
5
6
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
reacts to its own innovations but the effect is reducingsteadily. Also,
innovations to CPI, ER and GDPare having positive shocks on IDX. It also
indicates that IR and M1 slightly have a negative shock on stock market
returns of IDX.
4.8.4 The Philippine Stock Exchange (PSE)
Figure 14: Impulse Response Function of Log(PSE) to Shocks in System
138
8
9
10
7
8
9
10
7
8
9
10
7
8
9
10
9
10
-10,000
1
Figure 13 shows the result of IRF of macroeconomic variables on IDX. IDX
Macroeconomic Variables
7
0
-10,000
1
3
Response of M1 to M1
30,000
2
2
Response of M1 to IR
30,000
1
10
-.8
1
Response of M1 to GDP
30,000
-10,000
9
Response of IR to M1
.8
2
2
Response of IR to IR
.8
1
8
-4,000
1
.8
-.8
7
0
-4,000
1
6
Response of GDP to M1
12,000
2
2
Response of GDP to IR
12,000
1
5
-800
1
Response of GDP to GDP
12,000
-4,000
4
Response of ER to M1
800
1
2
Response of ER to IR
800
-800
3
Response of CPI to M1
1.0
1
2
Response of CPI to IR
7
8
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of MSE to M1
Response of MSE to IR
Response of MSE to GDP
Response of MSE to ER
Response of MSE to CPI
Response of MSE to MSE
400
400
400
400
400
400
200
200
200
200
200
200
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
1
1
1
0
0
0
0
0
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
4
5
6
7
8
9
1
10
2
4
5
6
7
8
9
3
4
5
6
7
8
1
10
9
3
4
5
6
7
8
9
1
10
2
2
1
1
1
1
1
1
0
0
0
0
0
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
5
6
7
8
9
1
10
2
4
5
6
7
8
9
2
3
4
5
6
7
8
9
1
10
0
0
0
0
-10,000
-10,000
-10,000
-10,000
-20,000
-20,000
-20,000
-20,000
-20,000
-20,000
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
.8
.8
.8
.8
.8
.4
.4
.4
.4
.4
.4
.0
.0
.0
.0
.0
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
1
10
40,000
40,000
40,000
40,000
20,000
20,000
20,000
20,000
20,000
20,000
0
0
0
0
0
0
-20,000
-20,000
-20,000
-20,000
-20,000
-20,000
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
-40,000
-40,000
-40,000
-40,000
-40,000
2
7
Response of M1 to M1
40,000
1
2
Response of M1 to IR
40,000
-40,000
6
.0
1
10
Response of M1 to GDP
Response of M1 to ER
Response of M1 to CPI
Response of M1 to MSE
3
5
-.4
-.4
-.4
-.4
-.4
1
4
Response of IR to M1
.8
-.4
2
Response of IR to IR
Response of IR to GDP
Response of IR to ER
Response of IR to CPI
3
10,000
10,000
0
-10,000
Response of IR to MSE
10
Response of GDP to M1
0
2
9
20,000
-10,000
1
2
Response of GDP to IR
20,000
10,000
8
0
1
10
Response of GDP to GDP
10,000
10,000
3
20,000
20,000
20,000
10,000
4
Response of GDP to ER
Response of GDP to CPI
Response of GDP to MSE
20,000
3
7
-1
-1
-1
-1
-1
4
6
Response of ER to M1
2
3
2
Response of ER to IR
2
2
5
0
2
2
1
4
-1
1
10
3
Response of CPI to M1
2
-1
2
1
Response of ER to GDP
Response of ER to ER
Response of ER to CPI
Response of ER to MSE
3
3
-1
-1
-1
-1
1
2
Response of CPI to IR
1
-1
-200
1
10
Response of CPI to GDP
Response of CPI to ER
Response of CPI to CPI
Response of CPI to MSE
3
0
0
-200
-200
-200
-200
1
0
0
0
0
-200
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
Figure 14 shows the outcomes of IRF of macroeconomic variables on PSE.
PSEreacts to its own innovations but the effect is reducingprogressively. Also,
innovations to CPI, GDP and IR are having negative shocks on PSE. It also
indicates that ER and M1 have a positive shock on stock market.
4.9 Conclusion
This chapter has presented the test results in both table form and figure forms. The
interpretations have been provided as well. Summary of the whole analysis will be
further discussed in the Chapter 5.
139
CHAPTER 5
DISCUSSION, CONCLUSION AND IMPLICATIONS
5.0 Introduction
In this chapter, the summary of findings from previous chapters will be presented in
table format as well as with interpretation. By referring to the major finding obtained
from previous chapter, the research objectives and hypotheses will be validated
accordingly.
Furthermore,
implications
of
this
study,
limitation
and
the
recommendations for future research will be discussed in this chapter. Lastly,
conclusion will be presented to end this paper.
5.1 Summary of Statistical Analysis
5.1.1 Summary of Econometric Problems
140
Table 46: Summary of Econometric Problems
Econometric
Problems
KLSE
Not passed,
Autocorrelatio there is
autocorrelation
n
problem
Passed, there
Heteroscedastic is no
heteroscedasti
ity
city problem
Passed, there
is no model
Model
specification
Specification
problem
Normality Test
Passed, model
is normally
distributed
Description On Results
SET
IDX
Not passed,
Not passed,
there is
there is
autocorrelation autocorrelation
problem
problem
Passed, there
Not passed,
is no
there is
heteroscedasti heteroscedasti
city problem
city problem
Passed, there
Passed, there
is no model
is no model
specification
specification
problem
problem
Not passed,
Passed, model
model is not
is normally
normally
distributed
distributed
PSE
Not passed,
there is
autocorrelation
problem
Passed, there
is no
heteroscedasti
city problem
Passed, there
is no model
specification
problem
Not passed,
model is not
normally
distributed
The econometric model for FTSE Bursa Malaysia (KLSE) passes through all
the econometric problem tests, except for autocorrelation.
The econometric model for The Bangkok Stock Exchange (SET) and The
Philippine Stock Exchange (PSE) pass through Heteroscedasticity test and
model specification test. However, it did not pass the autocorrelation test and
normality test.
The econometric model for Indonesia Stock Exchange (Bursa Efek Indonesia,
IDX) passes through model specification test and normality test. However, it
did not pass the autocorrelation test and Heteroscedasticity test.
141
5.1.2 Summary of Major Findings
5.1.2.1 FTSE Bursa Malaysia (KLSE)
Table 47: Summary of Major Findings (KLSE)
Dependent Independent
variable
Variable
Log(KLSE)
Log(CPI)
Log(KLSE)
Log(ER)
Log(KLSE)
Log(GDP)
Log(KLSE)
Log(IR)
Log(KLSE)
Log(M1)
Ordinary
Least
Square
Significant
at
1%
(negative)
Significant
at
1%
(negative)
Significant
at
1%
(negative)
Significant
at
1%
(positive)
Significant
at
1%
(positive)
Unit
Root
Test
Granger
Causality
Test
Impulse
Response
Function
Stationary
Significant
at
1%
Negative
shock
Stationary
Not
Significant
Negative
shock
Stationary
Not
Positive shock
Significant
Stationary
Not
Significant
Significant
Stationary
at
Positive shock
5%
Table 47 presents the relationship of the selected macroeconomic
variables and FTSE Bursa Malaysia (KLSE). Interest rate (IR) and
money supply (M1) are having positive relationship with KLCI. On
the other hand, Consumer Price Index (CPI), exchange rate (ER) and
142
Negative
shock
Gross Domestic Product (GDP) have a negative relationship with
KLCI and all these variables are significant at 1%.
All variables are stationary and do not contain unit root.
In terms of short run relationship, Consumer Price Index (CPI) and
money supply (M1) are having short run relationship with KLSEat
significance level of 1% and 5% respectively. However, exchange rate
(ER), Gross Domestic Product (GDP) and interest rate (IR) are
showing no relationship with FTSE Bursa Malaysia (KLSE) in short
run.
For Impulse Response Function, Consumer Price Index (CPI),
exchange rate (ER) and interest rate (IR) are having negative shock
towards KLSE, however, Gross Domestic Product (GDP) and money
supply (M1) are having positive shocks towards KLSE.
5.1.2.2 The Stock Exchange of Thailand (SET)
Table 48: Summary of Major Findings (SET)
Dependent Independent
variable
Variable
Log(SET)
Log(CPI)
Log(SET)
Log(ER)
143
Ordinary
Unit
Granger
Least
Root
Causality
Square
Test
Test
Significant
Significant
at
Stationary
at
5%
5%
(negative)
Not
Not
Stationary
Significant
Significant
Impulse
Response
Function
Negative
shock
Positive
shock
(negative)
Log(SET)
Log(GDP)
Log(SET)
Log(IR)
Log(SET)
Log(M1)
Not
Significant
Significant Stationary
at
(positive)
1%
Not
Significant
Significant Stationary
at
(positive)
5%
Significant
Significant
at
Stationary
at
1%
10%
(positive)
Table 48 presents the relationship of the selected macroeconomic
variables and The Stock Exchange of Thailand (SET). Gross Domestic
Product (GDP), Interest rate (IR) and money supply (M1) are having
positive relationship with SET, however, the relationship of exchange
rate (ER) and interest rate (IR) with SET is not significant. On the
other hand, Consumer Price Index (CPI) and exchange rate (ER) are
having negative relationship with SET.
All variables are stationary and do not contain unit root.
In terms of short run relationship, all variables have short run
relationship with The Stock Exchange of Thailand (SET) except for
exchange rate (ER).
For Impulse Response Function, Consumer Price Index (CPI) and
interest rate (IR) are having negative shock towards SET, however,
exchange rate (ER), Gross Domestic Product (GDP) and money supply
(M1) are having positive shocks towards SET.
144
Positive
shock
Negative
shock
Positive
shock
5.1.2.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 49: Summary of Major Findings (IDX)
Dependent Independent
variable
Variable
Log(IDX)
Log(CPI)
Log(IDX)
Log(ER)
Log(IDX)
Log(GDP)
Log(IDX)
Log(IR)
Log(IDX)
Log(M1)
Ordinary
Least
Square
Not
Significant
(positive)
Significant
at
1%
(negative)
Not
Significant
(positive)
Significant
at
1%
(negative)
Not
Significant
(positive)
Unit
Root
Test
Granger
Causality
Test
Impulse
Response
Function
Stationary
Not
Significant
Positive
shock
Stationary
Not
Significant
Positive
shock
Significant
Stationary
at
5%
Positive
shock
Stationary
Not
Significant
Negative
shock
Stationary
Not
Significant
Negative
shock
Table 49 presents the relationship of the selected macroeconomic
variables and Indonesia Stock Exchange (Bursa Efek Indonesia, IDX).
Consumer Price Index (CPI), Gross Domestic Product (GDP) and
money supply (M1) are having positive relationship with IDX,
however, all of the relationships are not significant. On the other hand,
and exchange rate (ER) and interest rate (IR) have a negative
relationship with IDX with the significance level of 1%.
All variables are stationary and do not contain unit root.
145
In terms of short run relationship, all variables do not have short run
relationship with IDX except for Gross Domestic Product (GDP).
For Impulse Response Function, Consumer Price Index (CPI),
exchange rate (ER) and Gross Domestic Product (GDP) are having
positive shock towards IDX, however, interest rate (IR), and money
supply (M1) are having negative shocks towards IDX.
5.1.2.4 The Philippine Stock Exchange (PSE)
Table 50: Summary of Major Findings (PSE)
Dependent Independent
variable
Variable
Log(PSE)
Log(CPI)
Log(PSE)
Log(ER)
Log(PSE)
Log(GDP)
Log(PSE)
Log(IR)
Log(PSE)
Log(M1)
146
Ordinary
Least
Square
Significant
at
1%
(negative)
Significant
at
1%
(negative)
Significant
at
1%
(positive)
Not
Significant
(positive)
Not
Significant
(negative)
Unit
Root
Test
Granger
Causality
Test
Impulse
Response
Function
Stationary
Not
Significant
Negative
shock
Stationary
Not
Positive shock
Significant
Stationary
Not
Significant
Negative
shock
Stationary
Not
Significant
Negative
shock
Stationary
Not
Positive shock
Significant
Table 50 presents the relationship of the selected macroeconomic
variables and The Philippine Stock Exchange (PSE). Gross Domestic
Product (GDP) and interest rate (IR) are having positive relationship
with PSE. On the other hand, Consumer Price Index (CPI), exchange
rate (ER) and money supply (M1) have a negative relationship with
PSE.
All variables are stationary and do not contain unit root.
In terms of short run relationship, all variables do not have short run
relationship with The Philippine Stock Exchange (PSE).
For Impulse Response Function, exchange rate (ER) and money
supply (M1) are having positive shock towards PSE. However,
Consumer Price Index (CPI), Gross Domestic Product (GDP) and
interest rate (IR) are having negative shocks towards PSE.
147
5.1.3 Summary of Long-run Relationship
5.1.3.1 FTSE Bursa Malaysia (KLSE)
Table 51: Summary of Long-run Relationship (KLSE)
Johansen Cointegration Test (KLSE)
Trace test
Max Eigenvalue Test
Cointegrated at r=0
No cointegration
Trace test is cointegrated at r=0 and this indicates that there is long run
relationship in this model (Refer Table 26).
5.1.3.2 The Stock Exchange of Thailand (SET)
Table 52: Summary of Long-run Relationship (SET)
Johansen Cointegration Test (SET)
Trace test
Max Eigenvalue Test
Cointegrated at r=0
Cointegrated at r=0
Both Trace test and Max Eigenvalue test is cointegrated at r=0. This
means that there is long run relationship in this model (Refer Table
148
27).
5.1.3.3 Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Table 53: Summary of Long-run Relationship (IDX)
Long-run Relationship Test: Johansen Cointegration Test
(IDX)
Trace test
Max Eigenvalue Test
Cointegrated at r=0
Cointegrated at r=0
Both Trace test and Max Eigenvalue test is cointegrated at r=0. This
means that there is long run relationship in this model (Refer Table
28).
5.1.3.4 The Philippine Stock Exchange (PSE)
Table 54: Summary of Long-run Relationship (PSE)
Long-run Relationship Test: Johansen Cointegration Test
(PSE)
Trace test
Max Eigenvalue Test
No cointegration
No cointegration
Both Trace test and Max Eigenvalue test are not cointegrated and this
149
indicates long run relationship does not exist in this model (Refer
Table 29).
5.2 Discussion of Major Findings
Table 55: Summary of Ordinary Least Square
Independent
Variables
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
Log(KLSE)
Significant at
1%
(negative)
Significant at
1%
(negative)
Significant at
1%
(negative)
Significant at
1%
(positive)
Significant at
1%
(positive)
Ordinary Least Square
Log(SET)
Log(IDX)
Significant at
Not
5%
Significant
(negative)
(positive)
Not
Significant at
Significant
1%
(negative)
(negative)
Not
Not
Significant
Significant
(positive)
(positive)
Not
Significant at
Significant
1%
(positive)
(negative)
Significant at
Not
1%
Significant
(positive)
(positive)
Log(PSE)
Significant at
1%
(negative)
Significant at
1%
(negative)
Significant at
1%
(positive)
Not
Significant
(positive)
Not
Significant
(negative)
Table 55 presents the major findings and results that derived from the testing done in
previous chapter. It clearly explain and show that the corresponding macroeconomic
variables for stock market returns.
150
According to the results, it indicates that there is a negative relationship between
Consumer Price Index (CPI) and stock returns which is supported by numerous
previous researches such as Hu et al (2000), Cauchie et al (2003), Ahmed et al(2012),
Al-Zoubi et al (2011) and Hasan (2008), which have been mentioned in Chapter 2.
From the study above, exchange rate (ER) also shows that there is negative
relationship with stock market returns and this applies to all four emerging nations
that being analyzed in this paper. This result is supported by Liu et al (2008) and
Wong et al (2002). Since these countries involve in the international trades, any
fluctuations in exchange rates will lead to certain impacts to both exports and imports.
Gross Domestic Product (GDP) shows that there is a positive relationship with stock
market returns and this is proved by Taulbee (2001) who argued that GDP is the
proxy of the purchasing power ability of investors and, therefore, higher purchasing
power ability will lead to greater stock market performance. However, result shows
that there is a negative relationship between Gross Domestic Product (GDP) and
FTSE Bursa Malaysia (KLSE). This result can be supported by the studies done by
Dimson et al (2002).
According to the result, there is always a negative relationship between interest rate
(IR) and stock return and this apply to Indonesia Stock Exchange (Bursa Efek
Indonesia, IDX) as result shows a negative relationship between interest rate (IR) and
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX). This is supported by the
result done by Alam et al. (2009) and he explained that when deposit interest rate
increases, userswill tend totransfer their funds or investments from stock market to
banks. Interestingly, result for FTSE Bursa Malaysia (KLSE) shows that it has a
positive relationship with interest rate (IR) and this can be backed by the research
done by Maysami et al (2004).
Shiblee (2009) claims that themovement in the stock prices are mainly set by status of
151
money supply naturallyseems right to agree that increases in the rate of money supply
will lead to an increase in stock prices and this is align with the result of positive
relationship with FTSE Bursa Malaysia (KLSE) and The Stock Exchange of Thailand
(SET). Money supply increases will lead to a greater liquidity and will eventually
bring down the interest rates and hence lead to an increase in aggregate demand and
ultimately increase the stock market returns. However, the result of The Philippine
Stock Exchange (PSE) shows a negative relationship with money supply and again
this is supported by the study completed by Wongbangpo and Sharma (2002) and
Theophano and Sunil (2006).
Table 56: Summary of Granger Causality Test
Independent
Variables
Log(CPI)
Log(ER)
Log(GDP)
Log(IR)
Log(M1)
Log(KLSE)
Significant
at
1%
Not
Significant
Granger Causality Test
Log(SET)
Log(IDX)
Log(PSE)
Significant at
5%
Not
Significant
Not
Significant
Not
Significant
Not
Significant
Not
Significant
Not
Significant
Significant at
1%
Not
Significant
Significant
at
5%
Significant at
5%
Not
Significant
Significant
at
5%
Not
Significant
Significant at
10%
Not
Significant
Not
Significant
Not
Significant
Short run relationship was also studied in this paper using Granger Causality test.
Result shows that the different macroeconomic variables have different short term
relationship with different stock markets. It is believed that the short term relationship
between these variables are vary for different stock markets and this is due to
different countries will be having different situation from time to time.
152
As per the results, Consumer Price Index (CPI) has significant relationship with
FTSE Bursa Malaysia (KLSE) and The Stock Exchange of Thailand (SET) in short
run.
As for exchange rate (ER), result shows that there is no relationship between
exchange rate (ER) and stock market returns in short run.
Gross Domestic Product (GDP) has significant short run relationship with The Stock
Exchange of Thailand (SET) and Indonesia Stock Exchange (Bursa Efek Indonesia,
IDX).
In terms of interest rate (IR), it has a short run relationship with The Stock Exchange
of Thailand (SET) only.
Lastly, result shows that money supply (M1) is having short run relationship with
FTSE Bursa Malaysia (KLSE) and The Stock Exchange of Thailand (SET).
Table 57: Summary of Johansen Cointegration Test
Tests
Trace test
Max
Eigenvalue
Test
Long-run Relationship Test: Johansen Cointegration Test
Log(KLSE)
Log(SET)
Log(IDX)
Log(PSE)
Cointegrated at
Cointegrated at r=0
Cointegrated at r=0
No cointegration
r=0
No cointegration
Cointegrated at
r=0
Cointegrated at r=0
No cointegration
The table above shows the long-run relationship between the respective emerging
countries’ stock markets and the macroeconomic variables.
Result shows that long-run relationships exist between the independent variables and
FTSE Bursa Malaysia. This is in line with the findings from Ibrahim (2003), who
153
utilized the stock market data from Bursa Malaysia with a set of similar
macroeconomic variables.
Result shows that long-run relationships exist between the independent variables and
The Stock Exchange of Thailand (SET). This is aligned with the research done by
Chowdhury (2004), who observed the long-run relationship between macroeconomic
variables and The Stock Exchange of Thailand (SET) from year 1990 until 2003.
Result shows that long-run relationships exist between the independent variables and
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX). This result is similar with
the analysis done by Abduh and Surur (2013), who investigated the long-run
relationship between economic activities and Indonesia Stock Exchange (Bursa Efek
Indonesia, IDX).
However, no long-run relationship exists between the independent variables and The
Philippines Stock Exchange (PSE) and this is supported by the studies done by
Chowdhury (2004), who observed the long-run relationship between macroeconomic
variables and The Philippines Stock Exchange (PSE) from year 1990 until 2003.
5.3 Implications of the Study
The results of this paper do contributesome practical information to the public and it
might be helpful to certain parties such as central bank, investors, policy makers and
economists. Therefore, stock market participants will have more understanding on the
stock market returns and its relationship with Consumer Price Index (CPI), exchange
rate (ER), Gross Domestic Product (GDP), interest rate (IR) and money supply (M1).
154
Based on OLS result attached in Table 5.2.1, different variables have different
relationship in different countries. Some of the variables, such as Consumer Price
Index (CPI) have negative relationship with stock market returns. However, some
variables such as Gross Domestic Product (GDP) have different relationship in
different country; it has negative relationship with FBM Bursa Malaysia (KLSE) but
positive relationship with The Philippine Stock Exchange (PSE).
Similarly to Granger Causality test that used to study the short term relationship
between stock market returns and a group of selected macroeconomic variables,
different variables have different short term relationship in different countries. This
information is helpful to the relevant in making short term decisions.
As for Variance Decomposition test, it shows the relationship between all the
variables and how these variables affect each other in both short and long run. By
using 10 periods in testing the relationship between all variables, results show that
most of them will influence by itself in the early stage, which is the short run, and
weaken in the later stage, which is the long run.However, the possibilities of being
influenced by other variables are tend to be greater in long run.
The result of Impulse Response Function shows the positive or negative impact to
stock market returns. Stock market participants can utilize this result to predict the
trend of the particular stock markets returns with the information of the
macroeconomic variables that being analyzed in this paper. However, there are still
challenges in forecastingthe stock market trend due to unforeseen uncertainty.
5.4 Limitations of the Study
155
Similar to all the research papers, this paper is facing certain limitations and
difficulties. The main limitation is the availability to obtain data as some data are not
fully ready such as the Gross Domestic Product (GDP). From Datastream, only
quarterly data is available for Gross Domestic Product (GDP). This could be one of
the restrictionsof gettingmore data period for this research. As a result, this research
paper is using quarterly data for all the variables for analysis and the observations
have been reduced to 60 instead of 180, if monthly data is available.
Moreover, this research paper only investigates on four emerging nations in Southeast
Asia, namely, Malaysia, Thailand, Indonesia and The Philippines. The findings and
resultspresented in this study are only helpful for the investors and policymakers of
the countries listed above. In addition, as a result of the different country status and
environment, culture,background, political factorsand other possible reasons, the
results of this paper might not be applicable toinvestors from different countries like
United Kingdom, Japan, China, Korea, United State and Europe. Hence, the findings
and results of this paper can only be the reference for different countries. It is not
encouraged to apply the findings to all countries.
This paper merelyconcentratesin studying the relationship between a group of
selected macroeconomic variables and selected stock markets in both short run and
long run. There are various macroeconomic variables that might have the possibilities
to affect these four countries are not measured and discussed in this paper. The result
of this paper might not be fullyhelpful for the relevant partiesin their decision making
process and therefore, they are encouraged not to solely refer to the result of this
paper only.
156
5.5 Recommendations for Future Research
Firstly, to ensure the reliability of the study, future researchers are suggested to solve
the entirediagnostic problem before proceed to analyze the relationship of the selected
variables.In addition, Panel data is suggested to use to apply in similar study to
overcome the disadvantages of employingtime series data.
Furthermore, the quarterly frequency is used in this paper and this might not fully
capture the effects for this paper. Future research should collect more frequent data
observations such as monthly or daily to improve the reliability of their study so that
the result and findings are more convincing to the relevant parties.
In addition, this paper only focus on five macroeconomic variables, Consumer Price
Index (CPI), exchange rate (ER), Gross Domestic Product (GDP), interest rate (IR)
and money supply (M1), other variables such as Foreign Direct Investment (FDI),
crude oil price (OP) and unemployment rate might bring impacts to a country’s stock
market returns as well. Thus, it is suggested to explore and study on different
macroeconomic variables that are not well documented by the previous studies.
Also, this paper is mainly focusing in Malaysia, Thailand, Indonesia and The
Philippines, which are not appropriate to apply across all countries, especially the
developed countries. Hence, it is better for future researchers tocollect additional data
or information toperform similar study by targeting on different country in order to
discover more possible cross-national and cross-cultural differences, which might
eventually lead to different results.
157
5.6 Conclusion
The macroeconomic model is examined to see whether macroeconomic factors can
explain the performance of stock market returns in emerging nations. The
macroeconomic variables used in this study include Consumer Price Index (CPI),
exchange rate (ER), Gross Domestic Product (GDP), interest rate (IR) and money
supply (M1). The stock markets used in this paper are FTSE Bursa Malaysia (KLSE),
The Stock Exchange of Thailand (SET), Indonesia Stock Exchange (Bursa Efek
Indonesia, IDX) and The Philippine Stock Exchange (PSE).
The result of FTSE Bursa Malaysia (KLSE) shows that there is positive relationship
between interest rate (IR) and money supply (M1) towards FTSE Bursa Malaysia
(KLSE). However, Consumer Prices Index (CPI), exchange rate (ER) and Gross
Domestic Product (GDP) have negative relationship with FTSE Bursa Malaysia
(KLSE).
The result of The Stock Exchange of Thailand (SET) shows that there is positive
relationship between money supply (M1) towards The Stock Exchange of Thailand
(SET). On the other hand, Consumer Prices Index (CPI) shows negative relationship
with The Stock Exchange of Thailand (SET). However, exchange rate (ER), Gross
Domestic Product (GDP) and interest rate (IR) do not have significant relationship
with The Stock Exchange of Thailand (SET).
The result of Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) shows that
there is negative relationship between exchange rate (ER) and interest rate (IR)
158
towards Indonesia Stock Exchange (Bursa Efek Indonesia, IDX). However,
Consumer Price Index (CPI), Gross Domestic Product (GDP) and money supply (M1)
do not have significant relationship with Indonesia Stock Exchange (Bursa Efek
Indonesia, IDX).
The result of The Philippine Stock Exchange (PSE) shows that there is positive
relationship between Gross Domestic Product (GDP) towards The Philippine Stock
Exchange (PSE). On the other hand, Consumer Prices Index (CPI) and exchange rate
(ER) have negative relationship with The Philippine Stock Exchange (PSE).
However, interest rate (IR) and money supply (M1) do not have significant
relationship with The Philippine Stock Exchange (PSE).
As a conclusion, this paper has achieved the main objective of investigating the
significant relationship between Consumer Price Index (CPI), exchange rate (ER),
Gross Domestic Product (GDP), interest rate (IR) and money supply (M1) towards
stock market returns of FTSE Bursa Malaysia (KLSE), The Stock Exchange of
Thailand (SET), Indonesia Stock Exchange (Bursa Efek Indonesia, IDX) and The
Philippine Stock Exchange (PSE).
159
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APPENDIX
APPENDIX A: Description Statistic
FTSE Bursa Malaysia (FBMKLCI)
LOG(KLCI)
6.987007
6.929862
7.532779
6.361317
0.345828
0.011304
1.712507
LOG(CPI)
4.536724
4.528823
4.714921
4.386185
0.103640
0.073454
1.608168
LOG(ER)
1.249676
1.263888
1.335001
1.099223
0.084693
-0.342366
1.533908
LOG(GDP)
11.93168
11.96734
12.50176
11.30680
0.376973
-0.175613
1.663520
LOG(IR)
1.752558
1.792591
2.050699
1.504818
0.162317
-0.009146
1.781640
LOG(M1)
11.92259
11.92793
12.70202
11.13748
0.497414
0.017291
1.718964
Jarque-Bera
Probability
4.145376
0.125847
4.896943
0.086426
6.545708
0.037898
4.773846
0.091912
3.711842
0.156309
4.105624
0.128373
Sum
Sum Sq. Dev.
419.2204
7.056222
272.2034
0.633735
74.98053
0.423199
715.9006
8.384393
105.1535
1.554462
715.3552
14.59783
Observations
60
60
60
60
60
60
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
The Stock Exchange of Thailand (SET)
LOG(SET)
6.536743
6.552409
7.355091
5.609411
0.499431
-0.160126
2.089608
LOG(CPI)
4.486985
4.491927
4.679535
4.306495
0.124430
0.007192
1.595939
LOG(ER)
3.586569
3.552010
3.816026
3.388394
0.132171
0.161566
1.570039
LOG(GDP)
13.81641
13.85293
14.05667
13.50751
0.173209
-0.336318
1.862082
LOG(IR)
1.894244
1.924249
2.079442
1.704748
0.107693
-0.496392
2.151030
LOG(M1)
6.829954
6.811643
7.395046
6.134771
0.379315
-0.180849
1.927592
Jarque-Bera
Probability
2.328439
0.312166
4.928984
0.085052
5.373005
0.068119
4.368238
0.112577
4.265926
0.118486
3.202213
0.201673
Sum
Sum Sq. Dev.
392.2046
14.71645
269.2191
0.913494
215.1941
1.030686
828.9844
1.770090
113.6546
0.684268
409.7972
8.488930
Observations
60
60
60
60
60
60
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
179
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
LOG(IDX)
7.321128
7.450659
8.540853
5.938430
0.887012
-0.196946
1.600951
LOG(CPI)
4.278051
4.322132
4.747364
3.676132
0.318260
-0.317112
1.848786
LOG(ER)
9.159970
9.132433
9.404632
8.921324
0.101396
0.827132
3.386322
LOG(GDP)
13.09737
13.08568
13.52191
12.73924
0.236654
0.129047
1.758669
LOG(IR)
2.674854
2.631528
2.974679
2.436825
0.163961
0.408243
1.891065
LOG(M1)
12.78631
12.82674
13.75401
11.72059
0.617671
-0.012381
1.710808
Jarque-Bera
Probability
5.281224
0.071318
4.318839
0.115392
7.214585
0.027125
4.018786
0.134070
4.740969
0.093435
4.156576
0.125144
Sum
Sum Sq. Dev.
439.2677
46.42065
256.6831
5.976061
549.5982
0.606590
785.8424
3.304302
160.4912
1.586104
767.1788
22.50955
Observations
60
60
60
60
60
60
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
The Philippine Stock Exchange (PSE)
LOG(MSE)
7.860044
7.806795
8.880229
6.939432
0.582296
0.195700
1.862221
LOG(CPI)
4.644645
4.633256
4.948050
4.323249
0.195274
-0.043711
1.624425
LOG(ER)
3.865954
3.860253
4.029243
3.706678
0.101662
0.152352
1.687692
LOG(GDP)
14.01604
14.03144
14.39847
13.67576
0.215220
0.089226
1.846536
LOG(IR)
2.125572
2.174745
2.575154
1.698669
0.246683
-0.397746
2.102691
LOG(M1)
13.59772
13.62944
14.58230
12.72432
0.572478
0.086978
1.734596
Jarque-Bera
Probability
3.619340
0.163708
4.749623
0.093032
4.537489
0.103442
3.405813
0.182153
3.594923
0.165719
4.078769
0.130109
Sum
Sum Sq. Dev.
471.6026
20.00502
278.6787
2.249776
231.9572
0.609769
840.9626
2.732854
127.5343
3.590289
815.8635
19.33613
Observations
60
60
60
60
60
60
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
APPENDIX B: Ordinary Least Square (OLS)
FTSE Bursa Malaysia (FBMKLCI)
180
Dependent Variable: LOG(KLCI)
Method: Least Squares
Date: 08/09/15 Time: 02:46
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
4.135989
-3.093987
-0.963392
-1.056170
1.410948
2.366988
2.371906
0.969224
0.296582
0.252321
0.236705
0.304980
1.743741
-3.192233
-3.248314
-4.185816
5.960773
7.761133
0.0869
0.0024
0.0020
0.0001
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.947681
0.942836
0.082684
0.369176
67.58844
195.6249
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
6.987007
0.345828
-2.052948
-1.843514
-1.971027
0.732732
The Stock Exchange of Thailand (SET)
Dependent Variable: LOG(SET)
Method: Least Squares
Date: 08/09/15 Time: 01:18
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-3.214831
-3.579738
-0.340678
0.986981
0.450098
1.836987
9.303501
1.552937
0.483235
0.796930
0.340546
0.494418
-0.345551
-2.305140
-0.704994
1.238480
1.321698
3.715457
0.7310
0.0250
0.4838
0.2209
0.1918
0.0005
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.877671
0.866344
0.182587
1.800254
20.05620
77.48624
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
6.536743
0.499431
-0.468540
-0.259105
-0.386619
0.613729
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
181
Dependent Variable: LOG(IDX)
Method: Least Squares
Date: 08/09/15 Time: 01:18
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-7.028513
0.629930
-1.068789
1.485780
-0.953241
0.354660
10.24108
0.539759
0.275682
1.354163
0.354217
0.703023
-0.686306
1.167059
-3.876886
1.097194
-2.691125
0.504479
0.4955
0.2483
0.0003
0.2774
0.0095
0.6160
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.963165
0.959754
0.177946
1.709903
21.60092
282.3996
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
7.321128
0.887012
-0.520031
-0.310596
-0.438109
0.284795
The Philippine Stock Exchange (PSE)
Dependent Variable: LOG(PSE)
Method: Least Squares
Date: 08/09/15 Time: 01:17
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-49.33752
-2.676674
-1.617534
5.877945
0.090379
-0.492329
8.301413
0.916703
0.236964
0.908947
0.190110
0.397412
-5.943268
-2.919893
-6.826087
6.466765
0.475404
-1.238837
0.0000
0.0051
0.0000
0.0000
0.6364
0.2208
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.951713
0.947242
0.133748
0.965981
38.73236
212.8630
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
182
7.860044
0.582296
-1.091079
-0.881644
-1.009157
0.713650
APPENDIX C: Breusch-Godfrey Serial Correlation LM Test
FTSE Bursa Malaysia (FBMKLCI)
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
21.76274
27.33856
Prob. F(2,52)
Prob. Chi-Square(2)
0.0000
0.0000
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 08/09/15 Time: 03:08
Sample: 2000Q1 2014Q4
Included observations: 60
Presample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
RESID(-1)
RESID(-2)
-0.664194
0.422844
0.108844
-0.315508
0.070405
0.188855
0.831911
-0.258195
1.786350
0.733021
0.226425
0.202345
0.178952
0.233073
0.131190
0.137155
-0.371817
0.576851
0.480708
-1.559260
0.393429
0.810284
6.341287
-1.882501
0.7115
0.5665
0.6327
0.1250
0.6956
0.4215
0.0000
0.0654
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.455643
0.382364
0.062167
0.200964
85.83292
6.217925
0.000026
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-1.13E-15
0.079103
-2.594431
-2.315185
-2.485202
1.996131
The Stock Exchange of Thailand (SET)
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
31.48747
32.86365
Prob. F(2,52)
Prob. Chi-Square(2)
183
0.0000
0.0000
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 08/09/15 Time: 03:09
Sample: 2000Q1 2014Q4
Included observations: 60
Presample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
RESID(-1)
RESID(-2)
13.86121
1.565426
-0.307881
-1.383295
-0.423474
0.019862
0.895077
-0.155963
6.974012
1.160731
0.350679
0.612686
0.269914
0.346336
0.127061
0.147637
1.987551
1.348655
-0.877956
-2.257756
-1.568923
0.057349
7.044483
-1.056395
0.0521
0.1833
0.3840
0.0282
0.1227
0.9545
0.0000
0.2957
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.547728
0.486845
0.125131
0.814205
43.86031
8.996421
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
5.29E-15
0.174679
-1.195344
-0.916098
-1.086115
1.569642
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
52.65282
40.16600
Prob. F(2,52)
Prob. Chi-Square(2)
0.0000
0.0000
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 08/09/15 Time: 03:10
Sample: 2000Q1 2014Q4
Included observations: 60
Presample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-2.641425
0.262820
-0.010861
0.495844
-0.128448
-0.354565
6.109359
0.319069
0.162294
0.819702
0.215454
0.426629
-0.432357
0.823709
-0.066924
0.604907
-0.596174
-0.831086
0.6673
0.4139
0.9469
0.5479
0.5536
0.4097
184
RESID(-1)
RESID(-2)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.950157
-0.165502
0.669433
0.624934
0.104259
0.565237
54.80933
15.04366
0.000000
0.137933
0.143536
6.888545
-1.153029
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.0000
0.2542
3.67E-15
0.170239
-1.560311
-1.281065
-1.451082
1.671907
The Philippine Stock Exchange (PSE)
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
20.47167
26.43116
Prob. F(2,52)
Prob. Chi-Square(2)
0.0000
0.0000
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 08/09/15 Time: 03:08
Sample: 2000Q1 2014Q4
Included observations: 60
Presample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
RESID(-1)
RESID(-2)
7.024183
0.350800
0.120052
-0.834881
-0.032486
0.195126
0.746367
-0.116922
6.917671
0.700983
0.187829
0.770475
0.145219
0.325432
0.135366
0.149788
1.015397
0.500440
0.639156
-1.083593
-0.223702
0.599592
5.513705
-0.780581
0.3146
0.6189
0.5255
0.2835
0.8239
0.5514
0.0000
0.4386
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.440519
0.365205
0.101947
0.540448
56.15475
5.849049
0.000049
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
185
-2.83E-14
0.127955
-1.605158
-1.325913
-1.495930
1.859280
APPENDIX D: Heteroskedasticity Test: Breusch-Pagan-Godfrey
FTSE Bursa Malaysia (FBMKLCI)
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
Obs*R-squared
Scaled explained SS
3.133568
13.49361
12.50144
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
0.0148
0.0192
0.0285
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 08/09/15 Time: 03:10
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-0.394777
0.242776
0.004806
-0.021234
0.005827
-0.038862
0.247747
0.101236
0.030978
0.026355
0.024724
0.031855
-1.593470
2.398122
0.155145
-0.805699
0.235684
-1.219955
0.1169
0.0200
0.8773
0.4240
0.8146
0.2278
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.224893
0.153124
0.008636
0.004028
203.1310
3.133568
0.014824
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.006153
0.009385
-6.571032
-6.361597
-6.489111
1.387463
The Stock Exchange of Thailand (SET)
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
Obs*R-squared
Scaled explained SS
0.346221
1.863705
3.835686
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
186
0.8825
0.8677
0.5733
Date: 08/09/15 Time: 03:11
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-4.239842
0.149110
0.093742
0.320076
-0.053999
-0.154529
3.575947
0.596896
0.185739
0.306313
0.130894
0.190037
-1.185655
0.249810
0.504695
1.044931
-0.412541
-0.813153
0.2409
0.8037
0.6158
0.3007
0.6816
0.4197
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.031062
-0.058655
0.070180
0.265964
77.42584
0.346221
0.882458
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.030004
0.068208
-2.380861
-2.171427
-2.298940
1.019558
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
Obs*R-squared
Scaled explained SS
5.214872
19.53761
17.74562
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
0.0006
0.0015
0.0033
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 08/09/15 Time: 03:12
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
-1.931525
0.012619
-0.174583
0.385886
0.111917
-0.144549
2.126122
0.112058
0.057234
0.281134
0.073538
0.145953
-0.908473
0.112616
-3.050349
1.372604
1.521889
-0.990380
0.3677
0.9108
0.0035
0.1755
0.1339
0.3264
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
0.325627
0.263185
0.036943
0.073698
115.9273
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
187
0.028498
0.043038
-3.664244
-3.454810
-3.582323
F-statistic
Prob(F-statistic)
5.214872
0.000562
Durbin-Watson stat
0.806196
The Philippine Stock Exchange (PSE)
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
Obs*R-squared
Scaled explained SS
2.175438
10.05949
14.97144
Prob. F(5,54)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
0.0704
0.0736
0.0105
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 08/09/15 Time: 03:11
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
0.329223
-0.025686
-0.021020
-0.117336
0.123626
0.093343
1.842162
0.203425
0.052584
0.201704
0.042187
0.088190
0.178716
-0.126265
-0.399737
-0.581726
2.930410
1.058438
0.8588
0.9000
0.6909
0.5632
0.0050
0.2946
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.167658
0.090589
0.029680
0.047569
129.0615
2.175438
0.070351
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
APPENDIX E: Model Specification Test
FTSE Bursa Malaysia (FBMKLCI)
Ramsey RESET Test
Equation: OLSMY
188
0.016100
0.031123
-4.102050
-3.892616
-4.020129
1.224943
Specification: LOG(KLCI) C LOG(CPI) LOG(ER) LOG(GDP) LOG(IR)
LOG(M1)
Omitted Variables: Squares of fitted values
t-statistic
F-statistic
Likelihood ratio
Value
0.721389
0.520401
0.586260
df
53
(1, 53)
1
Probability
0.4738
0.4738
0.4439
Sum of Sq.
0.003590
0.369176
0.365587
df
1
54
53
Mean
Squares
0.003590
0.006837
0.006898
Value
67.58844
67.88157
df
54
53
F-test summary:
Test SSR
Restricted SSR
Unrestricted SSR
LR test summary:
Restricted LogL
Unrestricted LogL
Unrestricted Test Equation:
Dependent Variable: LOG(KLCI)
Method: Least Squares
Date: 08/09/15 Time: 03:12
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
FITTED^2
5.116064
-7.846359
-2.499382
-2.838611
3.665531
6.169255
-0.112754
2.742651
6.659360
2.149952
2.483812
3.134370
5.279657
0.156301
1.865372
-1.178245
-1.162529
-1.142844
1.169464
1.168496
-0.721389
0.0677
0.2440
0.2502
0.2582
0.2474
0.2478
0.4738
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.948189
0.942324
0.083053
0.365587
67.88157
161.6596
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
The Stock Exchange of Thailand (SET)
Ramsey RESET Test
Equation: OLSTH
189
6.987007
0.345828
-2.029386
-1.785046
-1.933811
0.738855
Specification: LOG(SET) C LOG(CPI) LOG(ER) LOG(GDP) LOG(IR)
LOG(M1)
Omitted Variables: Squares of fitted values
t-statistic
F-statistic
Likelihood ratio
Value
1.574592
2.479340
2.743128
df
53
(1, 53)
1
Probability
0.1213
0.1213
0.0977
Sum of Sq.
0.080452
1.800254
1.719801
df
1
54
53
Mean
Squares
0.080452
0.033338
0.032449
Value
20.05620
21.42776
df
54
53
F-test summary:
Test SSR
Restricted SSR
Unrestricted SSR
LR test summary:
Restricted LogL
Unrestricted LogL
Unrestricted Test Equation:
Dependent Variable: LOG(SET)
Method: Least Squares
Date: 08/09/15 Time: 03:14
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
FITTED^2
11.04435
7.371896
0.775108
-1.401407
-1.219899
-4.336027
0.254350
12.89397
7.121965
0.854067
1.708489
1.112533
3.950618
0.161534
0.856551
1.035093
0.907549
-0.820261
-1.096505
-1.097557
1.574592
0.3955
0.3053
0.3682
0.4157
0.2778
0.2774
0.1213
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.883137
0.869908
0.180136
1.719801
21.42776
66.75405
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
6.536743
0.499431
-0.480925
-0.236585
-0.385351
0.773573
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Ramsey RESET Test
Equation: OLSID
190
Specification: LOG(IDX) C LOG(CPI) LOG(ER) LOG(GDP) LOG(IR)
LOG(M1)
Omitted Variables: Squares of fitted values
t-statistic
F-statistic
Likelihood ratio
Value
0.646221
0.417601
0.470903
df
53
(1, 53)
1
Probability
0.5209
0.5209
0.4926
Sum of Sq.
0.013367
1.709903
1.696536
df
1
54
53
Mean
Squares
0.013367
0.031665
0.032010
Value
21.60092
21.83637
df
54
53
F-test summary:
Test SSR
Restricted SSR
Unrestricted SSR
LR test summary:
Restricted LogL
Unrestricted LogL
Unrestricted Test Equation:
Dependent Variable: LOG(IDX)
Method: Least Squares
Date: 08/09/15 Time: 03:14
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
FITTED^2
5.534390
0.731230
-0.189554
0.083989
-0.349878
-0.220928
0.056225
21.99907
0.564879
1.388526
2.561101
0.999297
1.137091
0.087006
0.251574
1.294489
-0.136515
0.032794
-0.350125
-0.194292
0.646221
0.8023
0.2011
0.8919
0.9740
0.7276
0.8467
0.5209
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.963453
0.959316
0.178914
1.696536
21.83637
232.8645
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
The Philippine Stock Exchange (PSE)
Ramsey RESET Test
191
7.321128
0.887012
-0.494546
-0.250205
-0.398971
0.248285
Equation: OLSPH
Specification: LOG(PSE) C LOG(CPI) LOG(ER) LOG(GDP) LOG(IR)
LOG(M1)
Omitted Variables: Squares of fitted values
Value
0.624134
0.389543
0.439380
df
53
(1, 53)
1
Probability
0.5352
0.5352
0.5074
Sum of Sq.
0.007048
0.965981
0.958933
df
1
54
53
Mean
Squares
0.007048
0.017889
0.018093
Value
38.73236
38.95205
df
54
53
t-statistic
F-statistic
Likelihood ratio
F-test summary:
Test SSR
Restricted SSR
Unrestricted SSR
LR test summary:
Restricted LogL
Unrestricted LogL
Unrestricted Test Equation:
Dependent Variable: LOG(PSE)
Method: Least Squares
Date: 08/09/15 Time: 03:13
Sample: 2000Q1 2014Q4
Included observations: 60
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOG(CPI)
LOG(ER)
LOG(GDP)
LOG(IR)
LOG(M1)
FITTED^2
-96.42730
-5.242714
-2.974983
11.11115
0.105192
-0.916610
-0.054030
75.90871
4.213460
2.187950
8.434435
0.192661
0.788580
0.086568
-1.270306
-1.244278
-1.359712
1.317356
0.545993
-1.162355
-0.624134
0.2095
0.2189
0.1797
0.1934
0.5874
0.2503
0.5352
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.952065
0.946639
0.134510
0.958933
38.95205
175.4455
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
APPENDIX F: Normality Test
192
7.860044
0.582296
-1.065068
-0.820728
-0.969494
0.723537
FTSE Bursa Malaysia (FBMKLCI)
10
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
8
6
4
2
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-1.13e-15
0.003144
0.228564
-0.165345
0.079103
0.196883
3.287584
Jarque-Bera
Probability
0.594391
0.742899
0
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
The Stock Exchange of Thailand (SET)
12
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
10
8
6
4
2
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
5.29e-15
0.030121
0.260720
-0.643285
0.174679
-1.409465
6.081721
Jarque-Bera
Probability
43.60842
0.000000
0
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
193
9
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
8
7
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
3.67e-15
0.017461
0.419050
-0.424652
0.170239
-0.314889
3.242667
Jarque-Bera
Probability
1.138766
0.565875
0
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
The Philippine Stock Exchange (PSE)
9
Series: Residuals
Sample 2000Q1 2014Q4
Observations 60
8
7
6
5
4
3
2
1
0
-0.4
-0.3
-0.2
-0.1
0.0
0.1
APPENDIX G: Augmented Dickey-Fuller Test (Level)
FTSE Bursa Malaysia (FBMKLCI)
194
0.2
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-2.83e-14
0.020140
0.221734
-0.414529
0.127955
-1.112923
4.674791
Jarque-Bera
Probability
19.39830
0.000061
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
1.049576
-3.546099
-2.911730
-2.593551
0.9966
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:22
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
C
0.009940
-0.400278
0.009471
0.890808
1.049576
-0.449342
0.2983
0.6549
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.018960
0.001749
0.688827
27.04552
-60.70689
1.101610
0.298343
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
195
0.529944
0.689430
2.125657
2.196082
2.153149
1.594642
Null Hypothesis: ER has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.095165
-3.546099
-2.911730
-2.593551
0.7122
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER)
Method: Least Squares
Date: 08/09/15 Time: 01:24
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ER(-1)
C
-0.034015
0.111168
0.031059
0.109219
-1.095165
1.017851
0.2781
0.3130
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.020608
0.003426
0.069420
0.274691
74.68714
1.199387
0.278050
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
196
-0.008034
0.069539
-2.463971
-2.393546
-2.436480
1.581721
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Lag Length: 5 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
0.776701
-3.557472
-2.916566
-2.596116
0.9927
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:25
Sample (adjusted): 2001Q3 2014Q4
Included observations: 54 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
D(GDP(-1))
D(GDP(-2))
D(GDP(-3))
D(GDP(-4))
D(GDP(-5))
C
0.011519
0.310375
-0.371248
-0.354321
0.429337
-0.527953
2994.447
0.014831
0.126774
0.128980
0.141535
0.137865
0.137004
2418.678
0.776701
2.448259
-2.878337
-2.503416
3.114186
-3.853543
1.238051
0.4412
0.0181
0.0060
0.0158
0.0031
0.0004
0.2218
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.475651
0.408713
5461.141
1.40E+09
-537.5664
7.105831
0.000020
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
197
3400.963
7102.058
20.16913
20.42696
20.26856
2.020730
Null Hypothesis: IR has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.766552
-3.548208
-2.912631
-2.594027
0.3932
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:25
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
D(IR(-1))
C
-0.029957
0.422537
0.144515
0.016958
0.118351
0.099674
-1.766552
3.570217
1.449870
0.0829
0.0007
0.1528
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.238316
0.210618
0.116535
0.746927
43.91628
8.604215
0.000561
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
198
-0.052759
0.131164
-1.410906
-1.304331
-1.369393
2.032437
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Lag Length: 9 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
1.358552
-3.568308
-2.921175
-2.598551
0.9986
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:25
Sample (adjusted): 2002Q3 2014Q4
Included observations: 50 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
D(M1(-1))
D(M1(-2))
D(M1(-3))
D(M1(-4))
D(M1(-5))
D(M1(-6))
D(M1(-7))
D(M1(-8))
D(M1(-9))
C
0.030355
0.136301
0.061597
-0.207513
0.167144
-0.031759
-0.345384
-0.312197
0.551300
-0.483387
1309.523
0.022343
0.164279
0.172848
0.179899
0.166704
0.166857
0.165446
0.177432
0.179586
0.203106
1200.620
1.358552
0.829696
0.356363
-1.153494
1.002640
-0.190336
-2.087591
-1.759529
3.069844
-2.379977
1.090706
0.1821
0.4118
0.7235
0.2557
0.3222
0.8500
0.0434
0.0863
0.0039
0.0223
0.2821
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.606341
0.505403
3139.806
3.84E+08
-467.3312
6.007046
0.000019
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
199
4909.478
4464.541
19.13325
19.55389
19.29343
1.957590
Null Hypothesis: KLCI has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-0.921223
-3.548208
-2.912631
-2.594027
0.7746
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(KLCI)
Method: Least Squares
Date: 08/09/15 Time: 01:26
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
KLCI(-1)
D(KLCI(-1))
C
-0.020871
0.516835
31.18465
0.022656
0.121284
26.76398
-0.921223
4.261360
1.165172
0.3610
0.0001
0.2490
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.248464
0.221135
64.22069
226836.4
-322.1731
9.091705
0.000388
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
15.62412
72.76858
11.21287
11.31944
11.25438
1.922204
The Stock Exchange of Thailand (SET)
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Lag Length: 2 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
200
t-Statistic
Prob.*
0.473923
-3.550396
-2.913549
-2.594521
0.9844
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:41
Sample (adjusted): 2000Q4 2014Q4
Included observations: 57 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
D(CPI(-1))
D(CPI(-2))
C
0.005003
0.293506
-0.443057
0.209217
0.010557
0.125074
0.126454
0.939424
0.473923
2.346647
-3.503693
0.222707
0.6375
0.0227
0.0009
0.8246
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.224416
0.180515
0.832871
36.76470
-68.38187
5.111870
0.003515
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
201
0.569795
0.920041
2.539715
2.683087
2.595434
1.965234
Null Hypothesis: ER has a unit root
Exogenous: Constant
Lag Length: 2 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.091356
-3.550396
-2.913549
-2.594521
0.7134
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER)
Method: Least Squares
Date: 08/09/15 Time: 01:41
Sample (adjusted): 2000Q4 2014Q4
Included observations: 57 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ER(-1)
D(ER(-1))
D(ER(-2))
C
-0.028440
0.351758
-0.133346
0.911083
0.026059
0.127679
0.128082
0.960374
-1.091356
2.755005
-1.041099
0.948675
0.2801
0.0080
0.3026
0.3471
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.138984
0.090247
0.941000
46.93051
-75.33957
2.851718
0.045923
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
202
-0.153699
0.986570
2.783845
2.927217
2.839564
2.095475
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-0.822632
-3.546099
-2.911730
-2.593551
0.8052
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:41
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
C
-0.018259
27556.44
0.022195
22745.79
-0.822632
1.211496
0.4141
0.2307
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.011733
-0.005605
28475.59
4.62E+10
-687.8514
0.676723
0.414146
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
203
9095.220
28396.13
23.38479
23.45522
23.41228
2.532484
Null Hypothesis: IR has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-3.049079
-3.548208
-2.912631
-2.594027
0.0363
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:41
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
D(IR(-1))
C
-0.124040
0.535237
0.816219
0.040681
0.108440
0.272638
-3.049079
4.935787
2.993781
0.0035
0.0000
0.0041
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.359898
0.336621
0.211084
2.450599
9.460766
15.46188
0.000005
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
204
-0.021552
0.259164
-0.222785
-0.116210
-0.181272
2.032934
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Lag Length: 4 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-0.444493
-3.555023
-2.915522
-2.595565
0.8936
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:41
Sample (adjusted): 2001Q2 2014Q4
Included observations: 55 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
D(M1(-1))
D(M1(-2))
D(M1(-3))
D(M1(-4))
C
-0.004664
0.011498
-0.222632
-0.172787
0.618470
19.84797
0.010494
0.123869
0.125993
0.127897
0.128632
10.44425
-0.444493
0.092825
-1.767022
-1.350989
4.808071
1.900372
0.6586
0.9264
0.0835
0.1829
0.0000
0.0633
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.560354
0.515493
23.34585
26706.41
-248.1381
12.49068
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
205
19.17152
33.53973
9.241385
9.460367
9.326067
1.780559
Null Hypothesis: SET has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-0.465000
-3.548208
-2.912631
-2.594027
0.8901
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(SET)
Method: Least Squares
Date: 08/09/15 Time: 01:42
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
SET(-1)
D(SET(-1))
C
-0.013075
0.336933
24.46473
0.028117
0.130495
23.18938
-0.465000
2.581963
1.054997
0.6438
0.0125
0.2960
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.108897
0.076493
73.70710
298800.5
-330.1640
3.360624
0.041978
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
20.99448
76.69892
11.48841
11.59499
11.52993
1.870534
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
206
t-Statistic
Prob.*
0.580535
-3.546099
-2.911730
-2.593551
0.9880
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:46
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
C
0.003616
1.013460
0.006230
0.486732
0.580535
2.082171
0.5638
0.0418
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.005878
-0.011563
1.055967
63.55877
-85.91299
0.337021
0.563842
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
207
1.284520
1.049914
2.980101
3.050526
3.007592
2.023762
Null Hypothesis: ER has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.768532
-3.548208
-2.912631
-2.594027
0.3922
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER)
Method: Least Squares
Date: 08/09/15 Time: 01:46
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ER(-1)
D(ER(-1))
C
-0.159578
0.017105
1586.407
0.090232
0.137207
861.7009
-1.768532
0.124664
1.841018
0.0825
0.9012
0.0710
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.058236
0.023990
594.9214
19466232
-451.2872
1.700520
0.192047
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
208
63.81322
602.1885
15.66507
15.77165
15.70659
1.980809
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Lag Length: 4 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
2.489739
-3.555023
-2.915522
-2.595565
1.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:47
Sample (adjusted): 2001Q2 2014Q4
Included observations: 55 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
D(GDP(-1))
D(GDP(-2))
D(GDP(-3))
D(GDP(-4))
C
0.022685
-0.342091
-0.364392
-0.351551
0.643476
-1647.246
0.009112
0.118118
0.115615
0.115925
0.116430
2321.664
2.489739
-2.896184
-3.151761
-3.032575
5.526716
-0.709511
0.0162
0.0056
0.0028
0.0039
0.0000
0.4814
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.931254
0.924239
3141.924
4.84E+08
-517.7575
132.7537
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
209
7178.433
11414.92
19.04573
19.26471
19.13041
1.784850
Null Hypothesis: IR has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.627181
-3.548208
-2.912631
-2.594027
0.4625
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:47
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
D(IR(-1))
C
-0.035291
0.602727
0.488035
0.021688
0.099377
0.322169
-1.627181
6.065074
1.514843
0.1094
0.0000
0.1355
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.420323
0.399244
0.397231
8.678587
-27.21049
19.94023
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
210
-0.101868
0.512500
1.041741
1.148316
1.083254
1.661108
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Lag Length: 7 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
2.609448
-3.562669
-2.918778
-2.597285
1.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:47
Sample (adjusted): 2002Q1 2014Q4
Included observations: 52 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
D(M1(-1))
D(M1(-2))
D(M1(-3))
D(M1(-4))
D(M1(-5))
D(M1(-6))
D(M1(-7))
C
0.035698
-0.171405
0.012732
0.178917
0.754196
-0.305739
-0.322874
-0.504360
3100.294
0.013680
0.145175
0.147543
0.146775
0.127769
0.176995
0.181364
0.192752
3405.793
2.609448
-1.180678
0.086297
1.218992
5.902814
-1.727383
-1.780255
-2.616630
0.910300
0.0124
0.2442
0.9316
0.2295
0.0000
0.0913
0.0821
0.0122
0.3677
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.690971
0.633477
10957.50
5.16E+09
-552.5362
12.01818
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
211
14756.23
18099.26
21.59755
21.93526
21.72702
2.043683
Null Hypothesis: IDX has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
0.063547
-3.548208
-2.912631
-2.594027
0.9601
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IDX)
Method: Least Squares
Date: 08/09/15 Time: 01:47
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IDX(-1)
D(IDX(-1))
C
0.001197
0.296046
53.07477
0.018832
0.132899
47.09032
0.063547
2.227602
1.127084
0.9496
0.0300
0.2646
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.089609
0.056504
210.7293
2442376.
-391.0916
2.706804
0.075643
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
78.55458
216.9476
13.58936
13.69594
13.63088
1.913688
The Philippine Stock Exchange (PSE)
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
212
t-Statistic
Prob.*
0.423897
-3.548208
-2.912631
-2.594027
0.9824
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:32
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
D(CPI(-1))
C
0.002043
0.336222
0.525419
0.004819
0.130789
0.505890
0.423897
2.570710
1.038604
0.6733
0.0129
0.3035
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.122900
0.091005
0.705173
27.34982
-60.49817
3.853317
0.027155
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
213
1.120115
0.739631
2.189592
2.296167
2.231105
1.878725
Null Hypothesis: ER has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.678442
-3.548208
-2.912631
-2.594027
0.4367
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER)
Method: Least Squares
Date: 08/09/15 Time: 01:33
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ER(-1)
D(ER(-1))
C
-0.063938
0.312732
3.106270
0.038094
0.125848
1.843374
-1.678442
2.485005
1.685101
0.0989
0.0160
0.0976
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.129531
0.097878
1.396670
107.2878
-100.1355
4.092164
0.022040
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
214
0.043132
1.470487
3.556397
3.662972
3.597910
2.062191
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
3.287202
-3.546099
-2.911730
-2.593551
1.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:33
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
C
0.017705
-6350.954
0.005386
6830.991
3.287202
-0.929727
0.0017
0.3564
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.159363
0.144615
10801.89
6.65E+09
-630.6612
10.80569
0.001736
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
215
15622.90
11679.36
21.44614
21.51657
21.47363
1.758820
Null Hypothesis: IR has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-0.791443
-3.546099
-2.911730
-2.593551
0.8141
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:33
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
C
-0.031075
0.188752
0.039263
0.349368
-0.791443
0.540266
0.4320
0.5911
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.010870
-0.006483
0.598415
20.41170
-52.40521
0.626383
0.431966
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
216
-0.080791
0.596484
1.844244
1.914669
1.871736
1.690418
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Lag Length: 5 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
3.184925
-3.557472
-2.916566
-2.596116
1.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:33
Sample (adjusted): 2001Q3 2014Q4
Included observations: 54 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
D(M1(-1))
D(M1(-2))
D(M1(-3))
D(M1(-4))
D(M1(-5))
C
0.044613
0.340101
-0.103416
-0.292416
0.313069
-0.563124
-2943.004
0.014008
0.132794
0.134202
0.128263
0.134018
0.139691
8176.346
3.184925
2.561110
-0.770597
-2.279820
2.336020
-4.031204
-0.359941
0.0026
0.0137
0.4448
0.0272
0.0238
0.0002
0.7205
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.517299
0.455678
26014.57
3.18E+10
-621.8603
8.394806
0.000003
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
217
32786.12
35260.52
23.29112
23.54896
23.39056
1.977577
Null Hypothesis: MSE has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
0.578972
-3.548208
-2.912631
-2.594027
0.9880
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(MSE)
Method: Least Squares
Date: 08/09/15 Time: 01:33
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
MSE(-1)
D(MSE(-1))
C
0.012320
0.301899
32.79850
0.021278
0.132998
70.23262
0.578972
2.269942
0.466998
0.5650
0.0272
0.6423
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.117412
0.085318
265.5107
3877276.
-404.4942
3.658379
0.032236
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
96.85612
277.6176
14.05153
14.15810
14.09304
1.937849
APPENDIX H: Augmented Dickey-Fuller Test (First Difference)
FTSE Bursa Malaysia (FBMKLCI)
Null Hypothesis: D(CPI) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
218
t-Statistic
Prob.*
-5.988379
-3.548208
-2.912631
0.0000
10% level
-2.594027
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:27
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
C
-0.782223
0.425236
0.130623
0.112377
-5.988379
3.784027
0.0000
0.0004
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.390381
0.379495
0.681720
26.02554
-59.05886
35.86068
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
219
0.018391
0.865433
2.105478
2.176528
2.133153
1.887206
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-6.038101
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:27
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
C
-0.819046
-0.006319
0.135646
0.009252
-6.038101
-0.682935
0.0000
0.4975
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.394324
0.383508
0.069664
0.271769
73.23566
36.45866
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
220
0.002072
0.088724
-2.456402
-2.385352
-2.428727
1.952857
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Lag Length: 4 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.006029
-3.557472
-2.916566
-2.596116
0.0001
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:27
Sample (adjusted): 2001Q3 2014Q4
Included observations: 54 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
D(GDP(-1),2)
D(GDP(-2),2)
D(GDP(-3),2)
D(GDP(-4),2)
C
-1.423976
0.755281
0.401531
0.071738
0.513964
4644.438
0.284452
0.260933
0.210499
0.161283
0.135253
1151.548
-5.006029
2.894537
1.907516
0.444795
3.800017
4.033213
0.0000
0.0057
0.0624
0.6585
0.0004
0.0002
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.693982
0.662105
5438.525
1.42E+09
-537.9107
21.77068
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
221
267.5741
9355.998
20.14484
20.36584
20.23007
2.010472
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-4.646701
-3.548208
-2.912631
-2.594027
0.0004
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:28
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.557764
-0.029122
0.120034
0.016857
-4.646701
-1.727536
0.0000
0.0896
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.278274
0.265387
0.118721
0.789307
42.31580
21.59183
0.000021
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
222
0.000690
0.138516
-1.390200
-1.319150
-1.362525
2.020958
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Lag Length: 8 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.668040
-3.568308
-2.921175
-2.598551
0.4410
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:28
Sample (adjusted): 2002Q3 2014Q4
Included observations: 50 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
D(M1(-1),2)
D(M1(-2),2)
D(M1(-3),2)
D(M1(-4),2)
D(M1(-5),2)
D(M1(-6),2)
D(M1(-7),2)
D(M1(-8),2)
C
-0.374932
-0.416625
-0.211222
-0.271008
0.021314
0.093138
-0.138847
-0.314959
0.361139
1857.302
0.224774
0.247158
0.253474
0.273058
0.298224
0.298292
0.272477
0.236895
0.183999
1142.781
-1.668040
-1.685663
-0.833308
-0.992491
0.071470
0.312238
-0.509574
-1.329530
1.962719
1.625247
0.1031
0.0996
0.4096
0.3269
0.9434
0.7565
0.6131
0.1912
0.0567
0.1120
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.783822
0.735182
3172.822
4.03E+08
-468.4872
16.11474
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
223
7.235680
6165.551
19.13949
19.52189
19.28511
1.916126
Null Hypothesis: D(KLCI) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-4.361399
-3.548208
-2.912631
-2.594027
0.0009
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(KLCI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:28
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(KLCI(-1))
C
-0.511269
7.848156
0.117226
8.625259
-4.361399
0.909904
0.0001
0.3668
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.253550
0.240221
64.13385
230336.5
-322.6172
19.02180
0.000056
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-0.286397
73.57727
11.19370
11.26475
11.22137
1.883703
The Stock Exchange of Thailand (SET)
Null Hypothesis: D(CPI) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
224
t-Statistic
Prob.*
-7.242314
-3.550396
-2.913549
-2.594521
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:42
Sample (adjusted): 2000Q4 2014Q4
Included observations: 57 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
D(CPI(-1),2)
C
-1.134031
0.433143
0.649169
0.156584
0.123813
0.142973
-7.242314
3.498355
4.540489
0.0000
0.0009
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.502016
0.483572
0.826869
36.92050
-68.50240
27.21864
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
225
-0.016170
1.150621
2.508856
2.616385
2.550645
1.958178
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.581983
-3.550396
-2.913549
-2.594521
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:42
Sample (adjusted): 2000Q4 2014Q4
Included observations: 57 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
D(ER(-1),2)
C
-0.814679
0.157471
-0.128025
0.145948
0.126383
0.125820
-5.581983
1.245981
-1.017531
0.0000
0.2181
0.3134
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.404351
0.382290
0.942663
47.98517
-75.97296
18.32870
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
226
-0.036506
1.199401
2.770981
2.878510
2.812770
2.088390
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-9.921061
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:43
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
C
-1.274899
11528.10
0.128504
3827.698
-9.921061
3.011758
0.0000
0.0039
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.637370
0.630895
27783.64
4.32E+10
-674.7485
98.42745
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
227
34.53448
45731.34
23.33616
23.40721
23.36383
2.019022
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-4.310726
-3.548208
-2.912631
-2.594027
0.0010
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:43
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.498304
-0.010739
0.115596
0.029803
-4.310726
-0.360340
0.0001
0.7199
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.249152
0.235744
0.226181
2.864835
4.931604
18.58236
0.000067
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
228
-1.70E-17
0.258724
-0.101090
-0.030040
-0.073414
1.908771
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Lag Length: 3 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-2.503171
-3.555023
-2.915522
-2.595565
0.1202
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:43
Sample (adjusted): 2001Q2 2014Q4
Included observations: 55 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
D(M1(-1),2)
D(M1(-2),2)
D(M1(-3),2)
C
-0.841382
-0.164823
-0.406718
-0.598401
16.64472
0.336127
0.264211
0.187207
0.119476
7.498770
-2.503171
-0.623831
-2.172554
-5.008527
2.219661
0.0156
0.5356
0.0346
0.0000
0.0310
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.778829
0.761135
23.15776
26814.10
-248.2487
44.01729
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
229
-0.195758
47.38276
9.209045
9.391530
9.279613
1.758030
Null Hypothesis: D(SET) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.440427
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(SET,2)
Method: Least Squares
Date: 08/09/15 Time: 01:43
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(SET(-1))
C
-0.679293
14.73243
0.124860
9.914670
-5.440427
1.485922
0.0000
0.1429
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.345781
0.334099
73.18948
299975.2
-330.2778
29.59825
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
1.468736
89.68993
11.45785
11.52890
11.48553
1.864616
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Null Hypothesis: D(CPI) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
230
t-Statistic
Prob.*
-7.593718
-3.548208
-2.912631
-2.594027
0.0000
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:48
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
C
-1.010286
1.312896
0.133042
0.219683
-7.593718
5.976329
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.507322
0.498524
1.061771
63.13197
-84.75718
57.66455
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
231
0.023678
1.499359
2.991627
3.062677
3.019302
2.005041
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.539726
-3.550396
-2.913549
-2.594521
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:48
Sample (adjusted): 2000Q4 2014Q4
Included observations: 57 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
D(ER(-1),2)
C
-1.092591
0.014597
66.62122
0.197228
0.133614
82.69527
-5.539726
0.109249
0.805623
0.0000
0.9134
0.4240
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.538064
0.520956
616.3793
20515865
-445.4987
31.44969
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
232
1.730994
890.5537
15.73680
15.84433
15.77859
1.982408
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Lag Length: 3 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.987075
-3.555023
-2.915522
-2.595565
0.2916
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:48
Sample (adjusted): 2001Q2 2014Q4
Included observations: 55 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
D(GDP(-1),2)
D(GDP(-2),2)
D(GDP(-3),2)
C
-0.393291
-0.688067
-0.800057
-0.898574
3110.065
0.197925
0.152373
0.105756
0.058103
1385.611
-1.987075
-4.515682
-7.565106
-15.46519
2.244544
0.0524
0.0000
0.0000
0.0000
0.0293
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.969300
0.966844
3301.228
5.45E+08
-521.0333
394.6611
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
233
472.1818
18129.75
19.12848
19.31097
19.19905
2.012096
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-3.906182
-3.548208
-2.912631
-2.594027
0.0036
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:48
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.393760
-0.028910
0.100804
0.054293
-3.906182
-0.532476
0.0003
0.5965
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.214126
0.200093
0.403033
9.096377
-28.57400
15.25826
0.000255
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
234
0.018477
0.450630
1.054276
1.125326
1.081951
1.656344
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Lag Length: 3 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.343207
-3.555023
-2.915522
-2.595565
0.6032
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:48
Sample (adjusted): 2001Q2 2014Q4
Included observations: 55 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
D(M1(-1),2)
D(M1(-2),2)
D(M1(-3),2)
C
-0.260242
-0.893276
-0.893826
-0.910857
4407.061
0.193747
0.171122
0.134086
0.108052
3076.586
-1.343207
-5.220107
-6.666078
-8.429836
1.432452
0.1853
0.0000
0.0000
0.0000
0.1582
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.807589
0.792196
11599.45
6.73E+09
-590.1498
52.46522
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
235
340.4630
25445.50
21.64181
21.82430
21.71238
1.697569
Null Hypothesis: D(IDX) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.521440
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IDX,2)
Method: Least Squares
Date: 08/09/15 Time: 01:48
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IDX(-1))
C
-0.701737
55.41210
0.127093
29.14211
-5.521440
1.901445
0.0000
0.0624
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.352499
0.340936
208.8470
2442555.
-391.0937
30.48630
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.963821
257.2553
13.55495
13.62600
13.58263
1.914923
The Philippine Stock Exchange (PSE)
Null Hypothesis: D(CPI) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
236
t-Statistic
Prob.*
-5.154024
-3.548208
-2.912631
-2.594027
0.0001
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:34
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
C
-0.650936
0.727317
0.126297
0.169252
-5.154024
4.297255
0.0000
0.0001
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.321738
0.309626
0.699989
27.43917
-60.59276
26.56396
0.000003
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
237
-0.005172
0.842460
2.158371
2.229421
2.186046
1.890049
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.586595
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:34
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
C
-0.710165
0.027645
0.127120
0.186467
-5.586595
0.148258
0.0000
0.8827
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.357872
0.346406
1.419150
112.7832
-101.5841
31.21004
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
238
-0.010302
1.755392
3.571867
3.642917
3.599542
2.039990
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.632279
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:35
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
C
-0.728403
11368.32
0.129327
2534.146
-5.632279
4.486054
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.361624
0.350224
11441.57
7.33E+09
-623.2913
31.72257
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
239
-126.0000
14193.97
21.56177
21.63282
21.58944
1.971009
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-6.515548
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:36
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.862499
-0.070309
0.132376
0.079693
-6.515548
-0.882243
0.0000
0.3814
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.431197
0.421040
0.601244
20.24369
-51.77304
42.45236
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
240
0.000575
0.790181
1.854243
1.925292
1.881918
1.965233
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Lag Length: 4 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-2.878024
-3.557472
-2.916566
-2.596116
0.0546
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:36
Sample (adjusted): 2001Q3 2014Q4
Included observations: 54 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
D(M1(-1),2)
D(M1(-2),2)
D(M1(-3),2)
D(M1(-4),2)
C
-0.480530
-0.020838
0.063337
-0.083519
0.412400
15223.33
0.166965
0.188962
0.172761
0.164935
0.143404
6391.892
-2.878024
-0.110278
0.366616
-0.506374
2.875792
2.381663
0.0060
0.9126
0.7155
0.6149
0.0060
0.0212
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.532521
0.483825
28384.44
3.87E+10
-627.1368
10.93568
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
241
333.4489
39507.72
23.44951
23.67051
23.53474
1.849616
Null Hypothesis: D(MSE) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-5.421816
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(MSE,2)
Method: Least Squares
Date: 08/09/15 Time: 01:36
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(MSE(-1))
C
-0.671025
67.50687
0.123764
36.37214
-5.421816
1.856005
0.0000
0.0687
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.344232
0.332522
263.9300
3900907.
-404.6705
29.39609
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
7.641902
323.0504
14.02312
14.09417
14.05079
1.949998
APPENDIX I: Phillips-Perron Test (Level)
FTSE Bursa Malaysia (FBMKLCI)
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Bandwidth: 7 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
242
Adj. t-Stat
Prob.*
1.438231
-3.546099
-2.911730
0.9990
10% level
-2.593551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.458399
0.292537
Phillips-Perron Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:29
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
C
0.009940
-0.400278
0.009471
0.890808
1.049576
-0.449342
0.2983
0.6549
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.018960
0.001749
0.688827
27.04552
-60.70689
1.101610
0.298343
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
243
0.529944
0.689430
2.125657
2.196082
2.153149
1.594642
Null Hypothesis: ER has a unit root
Exogenous: Constant
Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-1.198216
-3.546099
-2.911730
-2.593551
0.6697
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.004656
0.006063
Phillips-Perron Test Equation
Dependent Variable: D(ER)
Method: Least Squares
Date: 08/09/15 Time: 01:30
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ER(-1)
C
-0.034015
0.111168
0.031059
0.109219
-1.095165
1.017851
0.2781
0.3130
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.020608
0.003426
0.069420
0.274691
74.68714
1.199387
0.278050
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
244
-0.008034
0.069539
-2.463971
-2.393546
-2.436480
1.581721
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Bandwidth: 58 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
2.342930
-3.546099
-2.911730
-2.593551
1.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
48058205
4767444.
Phillips-Perron Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:30
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
C
0.005096
2357.517
0.016237
2767.627
0.313835
0.851819
0.7548
0.3979
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.001725
-0.015789
7052.975
2.84E+09
-605.5111
0.098492
0.754792
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
245
3176.898
6997.947
20.59360
20.66402
20.62109
1.696355
Null Hypothesis: IR has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-1.667351
-3.546099
-2.911730
-2.593551
0.4423
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.015689
0.031077
Phillips-Perron Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:30
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
C
-0.032737
0.139377
0.017783
0.105594
-1.840918
1.319931
0.0708
0.1921
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.056119
0.039560
0.127436
0.925676
38.84830
3.388981
0.070840
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
246
-0.052599
0.130034
-1.249095
-1.178670
-1.221604
1.144060
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Bandwidth: 12 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
3.464760
-3.546099
-2.911730
-2.593551
1.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
17458808
11840506
Phillips-Perron Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:30
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
C
0.019363
1095.284
0.006944
1284.794
2.788533
0.852498
0.0072
0.3975
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.120043
0.104605
4251.047
1.03E+09
-575.6403
7.775914
0.007184
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
247
4328.540
4492.506
19.58103
19.65145
19.60852
1.996029
Null Hypothesis: KLCI has a unit root
Exogenous: Constant
Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-0.251454
-3.546099
-2.911730
-2.593551
0.9252
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
5187.680
8960.535
Phillips-Perron Test Equation
Dependent Variable: D(KLCI)
Method: Least Squares
Date: 08/09/15 Time: 01:30
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
KLCI(-1)
C
0.004893
8.923718
0.024966
29.92856
0.196005
0.298167
0.8453
0.7667
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.000674
-0.016858
73.27826
306073.1
-336.0616
0.038418
0.845303
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
14.48387
72.66828
11.45972
11.53014
11.48721
1.013119
The Stock Exchange of Thailand (SET)
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Bandwidth: 16 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
248
Adj. t-Stat
Prob.*
0.472929
0.9844
Test critical values:
1% level
5% level
10% level
-3.546099
-2.911730
-2.593551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.808789
0.455112
Phillips-Perron Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:44
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
C
0.002365
0.349072
0.010956
0.984843
0.215845
0.354444
0.8299
0.7243
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.000817
-0.016713
0.914968
47.71852
-77.45695
0.046589
0.829879
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
249
0.560085
0.907417
2.693456
2.763881
2.720947
1.565933
Null Hypothesis: ER has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-0.808965
-3.546099
-2.911730
-2.593551
0.8091
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.066285
1.427229
Phillips-Perron Test Equation
Dependent Variable: D(ER)
Method: Least Squares
Date: 08/09/15 Time: 01:44
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ER(-1)
C
-0.018641
0.593804
0.028341
1.043053
-0.657751
0.569294
0.5133
0.5714
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.007533
-0.009879
1.050571
62.91082
-85.61071
0.432636
0.513346
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
250
-0.086342
1.045420
2.969854
3.040279
2.997346
1.297072
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Bandwidth: 6 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-0.688217
-3.546099
-2.911730
-2.593551
0.8414
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
7.83E+08
3.03E+08
Phillips-Perron Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:44
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
C
-0.018259
27556.44
0.022195
22745.79
-0.822632
1.211496
0.4141
0.2307
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.011733
-0.005605
28475.59
4.62E+10
-687.8514
0.676723
0.414146
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
251
9095.220
28396.13
23.38479
23.45522
23.41228
2.532484
Null Hypothesis: IR has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-2.465778
-3.546099
-2.911730
-2.593551
0.1289
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.060336
0.128703
Phillips-Perron Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:44
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
C
-0.096384
0.623016
0.046434
0.312054
-2.075706
1.996500
0.0424
0.0507
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.070277
0.053966
0.249906
3.559823
-0.886492
4.308555
0.042443
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
252
-0.021186
0.256935
0.097847
0.168272
0.125338
0.978569
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Bandwidth: 20 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
0.489031
-3.546099
-2.911730
-2.593551
0.9850
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1076.298
472.3545
Phillips-Perron Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:44
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
C
0.001519
16.83131
0.012358
12.87497
0.122909
1.307290
0.9026
0.1964
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.000265
-0.017274
33.37759
63501.61
-289.6652
0.015107
0.902612
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
253
18.32090
33.09298
9.886957
9.957382
9.914448
1.954676
Null Hypothesis: SET has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
0.243883
-3.546099
-2.911730
-2.593551
0.9732
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
5810.209
6589.932
Phillips-Perron Test Equation
Dependent Variable: D(SET)
Method: Least Squares
Date: 08/09/15 Time: 01:45
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
SET(-1)
C
0.010455
11.46262
0.028276
23.81079
0.369759
0.481404
0.7129
0.6321
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.002393
-0.015109
77.55047
342802.3
-339.4048
0.136721
0.712933
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
19.43621
76.97118
11.57305
11.64347
11.60054
1.352193
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Bandwidth: 7 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
254
Adj. t-Stat
Prob.*
0.830883
-3.546099
-2.911730
0.9938
10% level
-2.593551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.077267
0.660221
Phillips-Perron Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:49
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
C
0.003616
1.013460
0.006230
0.486732
0.580535
2.082171
0.5638
0.0418
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.005878
-0.011563
1.055967
63.55877
-85.91299
0.337021
0.563842
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
255
1.284520
1.049914
2.980101
3.050526
3.007592
2.023762
Null Hypothesis: ER has a unit root
Exogenous: Constant
Bandwidth: 3 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-2.397494
-3.546099
-2.911730
-2.593551
0.1468
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
335003.4
390327.0
Phillips-Perron Test Equation
Dependent Variable: D(ER)
Method: Least Squares
Date: 08/09/15 Time: 01:49
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ER(-1)
C
-0.177149
1764.372
0.080420
768.9804
-2.202814
2.294430
0.0317
0.0255
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.078451
0.062284
588.8615
19765198
-459.0133
4.852389
0.031666
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
256
78.88983
608.1034
15.62757
15.69799
15.65506
1.898132
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Bandwidth: 11 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
7.786529
-3.546099
-2.911730
-2.593551
1.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.20E+08
6092806.
Phillips-Perron Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:49
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
C
0.017703
-1975.843
0.012609
6435.676
1.403930
-0.307014
0.1658
0.7600
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.033424
0.016466
11167.11
7.11E+09
-632.6230
1.971019
0.165765
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
257
6825.834
11260.20
21.51264
21.58307
21.54013
2.632800
Null Hypothesis: IR has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-1.864450
-3.546099
-2.911730
-2.593551
0.3465
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.256200
0.570408
Phillips-Perron Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:49
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
C
-0.049328
0.608153
0.027162
0.406041
-1.816071
1.497765
0.0746
0.1397
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.054697
0.038113
0.514966
15.11582
-43.54440
3.298115
0.074620
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
258
-0.119124
0.525069
1.543878
1.614303
1.571369
0.727839
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Bandwidth: 18 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
4.742781
-3.546099
-2.911730
-2.593551
1.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
2.61E+08
85259829
Phillips-Perron Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:50
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
C
0.022381
4471.968
0.008815
4269.480
2.538841
1.047427
0.0139
0.2993
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.101594
0.085833
16435.19
1.54E+10
-655.4237
6.445712
0.013877
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
259
13852.02
17189.45
22.28555
22.35597
22.31304
2.333460
Null Hypothesis: IDX has a unit root
Exogenous: Constant
Bandwidth: 3 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
0.538577
-3.546099
-2.911730
-2.593551
0.9867
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
45589.33
57904.79
Phillips-Perron Test Equation
Dependent Variable: D(IDX)
Method: Least Squares
Date: 08/09/15 Time: 01:50
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IDX(-1)
C
0.013986
46.43954
0.018585
47.77395
0.752551
0.972068
0.4548
0.3351
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.009838
-0.007533
217.2302
2689771.
-400.1765
0.566333
0.454817
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
75.41555
216.4166
13.63310
13.70353
13.66059
1.422356
The Philippine Stock Exchange (PSE)
Null Hypothesis: CPI has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat
260
Prob.*
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
0.962931
-3.546099
-2.911730
-2.593551
0.9957
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.522799
0.688811
Phillips-Perron Test Equation
Dependent Variable: D(CPI)
Method: Least Squares
Date: 08/09/15 Time: 01:37
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CPI(-1)
C
0.005511
0.528766
0.004795
0.514317
1.149442
1.028094
0.2552
0.3082
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.022654
0.005508
0.735624
30.84515
-64.58491
1.321217
0.255174
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
261
1.109605
0.737658
2.257116
2.327541
2.284607
1.318974
262
Null Hypothesis: GDP has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
3.131207
-3.546099
-2.911730
-2.593551
1.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.13E+08
1.23E+08
Phillips-Perron Test Equation
Dependent Variable: D(GDP)
Method: Least Squares
Date: 08/09/15 Time: 01:37
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
GDP(-1)
C
0.017705
-6350.954
0.005386
6830.991
3.287202
-0.929727
0.0017
0.3564
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.159363
0.144615
10801.89
6.65E+09
-630.6612
10.80569
0.001736
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
263
15622.90
11679.36
21.44614
21.51657
21.47363
1.758820
Null Hypothesis: IR has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-0.849178
-3.546099
-2.911730
-2.593551
0.7973
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.345961
0.373624
Phillips-Perron Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Date: 08/09/15 Time: 01:38
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
IR(-1)
C
-0.031075
0.188752
0.039263
0.349368
-0.791443
0.540266
0.4320
0.5911
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.010870
-0.006483
0.598415
20.41170
-52.40521
0.626383
0.431966
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
264
-0.080791
0.596484
1.844244
1.914669
1.871736
1.690418
Null Hypothesis: M1 has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
4.155296
-3.546099
-2.911730
-2.593551
1.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
8.60E+08
9.89E+08
Phillips-Perron Test Equation
Dependent Variable: D(M1)
Method: Least Squares
Date: 08/09/15 Time: 01:39
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
M1(-1)
C
0.034248
-775.3727
0.007633
8037.636
4.487100
-0.096468
0.0000
0.9235
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.261027
0.248062
29843.09
5.08E+10
-690.6188
20.13406
0.000036
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
265
30796.89
34415.40
23.47860
23.54903
23.50610
1.704064
Null Hypothesis: MSE has a unit root
Exogenous: Constant
Bandwidth: 0 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
1.526273
-3.546099
-2.911730
-2.593551
0.9992
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
74094.56
74094.56
Phillips-Perron Test Equation
Dependent Variable: D(MSE)
Method: Least Squares
Date: 08/09/15 Time: 01:39
Sample (adjusted): 2000Q2 2014Q4
Included observations: 59 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
MSE(-1)
C
0.031598
-4.527398
0.020703
71.70838
1.526273
-0.063136
0.1325
0.9499
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.039264
0.022409
276.9375
4371579.
-414.5037
2.329508
0.132472
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
APPENDIX J: Phillips-Perron Test (First Difference)
FTSE Bursa Malaysia (FBMKLCI)
Null Hypothesis: D(CPI) has a unit root
266
90.07919
280.0935
14.11877
14.18920
14.14626
1.406208
Exogenous: Constant
Bandwidth: 9 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.857968
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.448716
0.210568
Phillips-Perron Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:31
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
C
-0.782223
0.425236
0.130623
0.112377
-5.988379
3.784027
0.0000
0.0004
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.390381
0.379495
0.681720
26.02554
-59.05886
35.86068
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
267
0.018391
0.865433
2.105478
2.176528
2.133153
1.887206
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-6.031283
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.004686
0.004648
Phillips-Perron Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:31
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
C
-0.819046
-0.006319
0.135646
0.009252
-6.038101
-0.682935
0.0000
0.4975
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.394324
0.383508
0.069664
0.271769
73.23566
36.45866
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
268
0.002072
0.088724
-2.456402
-2.385352
-2.428727
1.952857
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Bandwidth: 28 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-8.015529
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
47717537
7812410.
Phillips-Perron Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:31
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
C
-0.843575
2717.177
0.132025
1011.835
-6.389489
2.685396
0.0000
0.0095
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.421640
0.411313
7030.059
2.77E+09
-595.0419
40.82558
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
269
69.50000
9162.555
20.58765
20.65870
20.61533
1.845809
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-4.638299
-3.548208
-2.912631
-2.594027
0.0004
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.013609
0.013505
Phillips-Perron Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:32
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.557764
-0.029122
0.120034
0.016857
-4.646701
-1.727536
0.0000
0.0896
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.278274
0.265387
0.118721
0.789307
42.31580
21.59183
0.000021
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
270
0.000690
0.138516
-1.390200
-1.319150
-1.362525
2.020958
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-6.844980
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
18906620
18474782
Phillips-Perron Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:32
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
C
-0.896724
4006.663
0.130879
819.7370
-6.851540
4.887742
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.456013
0.446299
4425.139
1.10E+09
-568.1941
46.94360
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
271
44.89319
5946.880
19.66187
19.73292
19.68954
2.013348
Null Hypothesis: D(KLCI) has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-4.176679
-3.548208
-2.912631
-2.594027
0.0016
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
3971.318
3339.862
Phillips-Perron Test Equation
Dependent Variable: D(KLCI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:32
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(KLCI(-1))
C
-0.511269
7.848156
0.117226
8.625259
-4.361399
0.909904
0.0001
0.3668
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.253550
0.240221
64.13385
230336.5
-322.6172
19.02180
0.000056
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-0.286397
73.57727
11.19370
11.26475
11.22137
1.883703
The Stock Exchange of Thailand (SET)
Null Hypothesis: D(CPI) has a unit root
Exogenous: Constant
Bandwidth: 57 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
272
Adj. t-Stat
Prob.*
-7.900402
-3.548208
-2.912631
0.0000
10% level
-2.594027
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.781250
0.091961
Phillips-Perron Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:45
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
C
-0.788995
0.448858
0.131265
0.140195
-6.010717
3.201677
0.0000
0.0023
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.392155
0.381300
0.899528
45.31248
-75.13948
36.12872
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
273
-0.005086
1.143602
2.659982
2.731032
2.687657
1.803405
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Bandwidth: 6 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.133133
-3.548208
-2.912631
-2.594027
0.0001
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.950211
0.711881
Phillips-Perron Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:45
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
C
-0.667059
-0.073424
0.125001
0.130827
-5.336411
-0.561231
0.0000
0.5769
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.337100
0.325262
0.992042
55.11225
-80.81737
28.47729
0.000002
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
274
-0.008578
1.207710
2.855772
2.926821
2.883447
1.870149
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Bandwidth: 6 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-11.27176
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
7.45E+08
3.94E+08
Phillips-Perron Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:45
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
C
-1.274899
11528.10
0.128504
3827.698
-9.921061
3.011758
0.0000
0.0039
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.637370
0.630895
27783.64
4.32E+10
-674.7485
98.42745
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
275
34.53448
45731.34
23.33616
23.40721
23.36383
2.019022
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-4.291386
-3.548208
-2.912631
-2.594027
0.0011
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.049394
0.048555
Phillips-Perron Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:45
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.498304
-0.010739
0.115596
0.029803
-4.310726
-0.360340
0.0001
0.7199
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.249152
0.235744
0.226181
2.864835
4.931604
18.58236
0.000067
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
276
-1.70E-17
0.258724
-0.101090
-0.030040
-0.073414
1.908771
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Bandwidth: 18 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-8.138447
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1058.391
490.5526
Phillips-Perron Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:46
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
C
-0.994090
18.99994
0.131618
4.988360
-7.552857
3.808854
0.0000
0.0003
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.504625
0.495779
33.10877
61386.68
-284.2691
57.04565
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
277
0.524138
46.62647
9.871348
9.942398
9.899023
2.010325
Null Hypothesis: D(SET) has a unit root
Exogenous: Constant
Bandwidth: 7 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.176594
-3.548208
-2.912631
-2.594027
0.0001
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
5171.986
2820.987
Phillips-Perron Test Equation
Dependent Variable: D(SET,2)
Method: Least Squares
Date: 08/09/15 Time: 01:46
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(SET(-1))
C
-0.679293
14.73243
0.124860
9.914670
-5.440427
1.485922
0.0000
0.1429
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.345781
0.334099
73.18948
299975.2
-330.2778
29.59825
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
1.468736
89.68993
11.45785
11.52890
11.48553
1.864616
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Null Hypothesis: D(CPI) has a unit root
Exogenous: Constant
Bandwidth: 6 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
278
Adj. t-Stat
Prob.*
-7.676252
-3.548208
-2.912631
0.0000
10% level
-2.594027
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.088482
0.813169
Phillips-Perron Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:50
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
C
-1.010286
1.312896
0.133042
0.219683
-7.593718
5.976329
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.507322
0.498524
1.061771
63.13197
-84.75718
57.66455
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
279
0.023678
1.499359
2.991627
3.062677
3.019302
2.005041
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-8.146429
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
354710.8
350937.5
Phillips-Perron Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:50
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
C
-1.067259
68.83156
0.131069
80.18576
-8.142714
0.858401
0.0000
0.3943
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.542124
0.533947
606.1180
20573225
-452.8911
66.30379
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
280
-10.79885
887.8502
15.68590
15.75695
15.71358
2.015399
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Bandwidth: 16 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-9.866724
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.17E+08
1.12E+08
Phillips-Perron Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:51
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
C
-1.276236
8759.442
0.130076
1677.224
-9.811452
5.222584
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.632219
0.625652
10988.62
6.76E+09
-620.9485
96.26460
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
281
369.7121
17959.95
21.48098
21.55203
21.50866
2.147414
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Bandwidth: 0 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-3.906182
-3.548208
-2.912631
-2.594027
0.0036
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.156834
0.156834
Phillips-Perron Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:51
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.393760
-0.028910
0.100804
0.054293
-3.906182
-0.532476
0.0003
0.5965
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.214126
0.200093
0.403033
9.096377
-28.57400
15.25826
0.000255
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
282
0.018477
0.450630
1.054276
1.125326
1.081951
1.656344
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-7.692338
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
2.95E+08
2.93E+08
Phillips-Perron Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:51
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
C
-1.027101
14336.28
0.133528
2938.619
-7.692023
4.878576
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.513751
0.505068
17465.62
1.71E+10
-647.8242
59.16722
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
283
202.9621
24826.25
22.40773
22.47878
22.43541
2.007267
Null Hypothesis: D(IDX) has a unit root
Exogenous: Constant
Bandwidth: 6 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.272251
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
42113.02
26969.72
Phillips-Perron Test Equation
Dependent Variable: D(IDX,2)
Method: Least Squares
Date: 08/09/15 Time: 01:51
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IDX(-1))
C
-0.701737
55.41210
0.127093
29.14211
-5.521440
1.901445
0.0000
0.0624
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.352499
0.340936
208.8470
2442555.
-391.0937
30.48630
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.963821
257.2553
13.55495
13.62600
13.58263
1.914923
The Philippine Stock Exchange (PSE)
Null Hypothesis: D(CPI) has a unit root
Exogenous: Constant
Bandwidth: 5 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
284
Adj. t-Stat
Prob.*
-4.994910
-3.548208
0.0001
5% level
10% level
-2.912631
-2.594027
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.473089
0.397239
Phillips-Perron Test Equation
Dependent Variable: D(CPI,2)
Method: Least Squares
Date: 08/09/15 Time: 01:39
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(CPI(-1))
C
-0.650936
0.727317
0.126297
0.169252
-5.154024
4.297255
0.0000
0.0001
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.321738
0.309626
0.699989
27.43917
-60.59276
26.56396
0.000003
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
285
-0.005172
0.842460
2.158371
2.229421
2.186046
1.890049
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
Bandwidth: 3 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.569601
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.944538
1.904386
Phillips-Perron Test Equation
Dependent Variable: D(ER,2)
Method: Least Squares
Date: 08/09/15 Time: 01:39
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(ER(-1))
C
-0.710165
0.027645
0.127120
0.186467
-5.586595
0.148258
0.0000
0.8827
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.357872
0.346406
1.419150
112.7832
-101.5841
31.21004
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
286
-0.010302
1.755392
3.571867
3.642917
3.599542
2.039990
Null Hypothesis: D(GDP) has a unit root
Exogenous: Constant
Bandwidth: 3 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.677034
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
1.26E+08
1.33E+08
Phillips-Perron Test Equation
Dependent Variable: D(GDP,2)
Method: Least Squares
Date: 08/09/15 Time: 01:40
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(GDP(-1))
C
-0.728403
11368.32
0.129327
2534.146
-5.632279
4.486054
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.361624
0.350224
11441.57
7.33E+09
-623.2913
31.72257
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
287
-126.0000
14193.97
21.56177
21.63282
21.58944
1.971009
Null Hypothesis: D(IR) has a unit root
Exogenous: Constant
Bandwidth: 8 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-6.540864
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
0.349029
0.176339
Phillips-Perron Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Date: 08/09/15 Time: 01:40
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(IR(-1))
C
-0.862499
-0.070309
0.132376
0.079693
-6.515548
-0.882243
0.0000
0.3814
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.431197
0.421040
0.601244
20.24369
-51.77304
42.45236
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
288
0.000575
0.790181
1.854243
1.925292
1.881918
1.965233
Null Hypothesis: D(M1) has a unit root
Exogenous: Constant
Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.019238
-3.548208
-2.912631
-2.594027
0.0001
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
9.97E+08
1.03E+09
Phillips-Perron Test Equation
Dependent Variable: D(M1,2)
Method: Least Squares
Date: 08/09/15 Time: 01:40
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(M1(-1))
C
-0.611792
19227.17
0.122586
5663.378
-4.990712
3.395001
0.0000
0.0013
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.307849
0.295489
32129.32
5.78E+10
-683.1772
24.90721
0.000006
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
289
370.6708
38278.74
23.62680
23.69785
23.65447
2.195072
Null Hypothesis: D(MSE) has a unit root
Exogenous: Constant
Bandwidth: 3 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-5.366121
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction)
HAC corrected variance (Bartlett kernel)
67257.01
62544.93
Phillips-Perron Test Equation
Dependent Variable: D(MSE,2)
Method: Least Squares
Date: 08/09/15 Time: 01:40
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(MSE(-1))
C
-0.671025
67.50687
0.123764
36.37214
-5.421816
1.856005
0.0000
0.0687
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.344232
0.332522
263.9300
3900907.
-404.6705
29.39609
0.000001
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
APPENDIX I: Johansen-Juselius Cointegration Tests
FTSE Bursa Malaysia (FBMKLCI)
Date: 08/09/15 Time: 02:10
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Trend assumption: Linear deterministic trend
290
7.641902
323.0504
14.02312
14.09417
14.05079
1.949998
Series: LOG(KLCI) LOG(CPI) LOG(ER) LOG(GDP) LOG(IR) LOG(M1)
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
At most 2
At most 3
At most 4
At most 5
0.448071
0.371125
0.287036
0.221478
0.042040
0.004209
98.25244
63.78095
36.87925
17.25646
2.735725
0.244654
95.75366
69.81889
47.85613
29.79707
15.49471
3.841466
0.0333
0.1379
0.3532
0.6211
0.9776
0.6209
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None
At most 1
At most 2
At most 3
At most 4
At most 5
0.448071
0.371125
0.287036
0.221478
0.042040
0.004209
34.47149
26.90170
19.62280
14.52073
2.491071
0.244654
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
0.1870
0.2686
0.3678
0.3239
0.9747
0.6209
Max-eigenvalue test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
LOG(KLCI)
-8.253439
-15.79162
2.343345
5.451378
1.401299
2.775410
LOG(CPI)
2.893062
-65.84209
99.00621
-29.48541
9.282468
39.24989
LOG(ER)
-18.98725
-3.133337
13.83187
9.558550
-21.57643
13.77222
LOG(GDP)
5.954816
-16.40332
5.245879
27.67101
7.172849
-8.772684
LOG(IR)
12.88184
17.09832
-13.59350
-16.50004
-16.37207
9.175761
LOG(M1)
1.233844
41.78698
-28.16021
-21.16362
-15.57202
3.239558
0.021199
0.000334
0.003757
-0.003131
-0.001778
1.32E-05
0.005506
-0.003254
-0.001608
-0.005087
0.002587
-0.001544
0.008166
0.000863
-0.004714
-0.007691
0.004422
-0.000338
-0.001750
9.26E-05
0.002185
0.000811
0.001116
0.001786
Log likelihood
901.0241
Unrestricted Adjustment Coefficients (alpha):
D(LOG(KLCI))
D(LOG(CPI))
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
0.000956
-0.000672
0.002562
-0.007938
-0.009322
0.003918
1 Cointegrating Equation(s):
291
-0.002449
-2.24E-05
0.000527
-0.001446
-0.000111
-0.001315
Normalized cointegrating coefficients (standard error in parentheses)
LOG(KLCI)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
-0.350528
2.300525
-0.721495
(2.13533)
(0.61975)
(0.56126)
LOG(IR)
-1.560784
(0.46182)
LOG(M1)
-0.149495
(0.65334)
LOG(IR)
-1.523712
(0.42636)
0.105761
(0.11978)
LOG(M1)
-0.343113
(0.51165)
-0.552362
(0.14374)
LOG(IR)
-0.725324
(0.32826)
-0.067949
(0.05030)
-0.373514
(0.21260)
LOG(M1)
-1.407857
(0.36651)
-0.320698
(0.05616)
0.498125
(0.23737)
Adjustment coefficients (standard error in parentheses)
D(LOG(KLCI))
-0.007888
(0.06516)
D(LOG(CPI))
0.005549
(0.00751)
D(LOG(ER))
-0.021143
(0.02137)
D(LOG(GDP))
0.065519
(0.03642)
D(LOG(IR))
0.076940
(0.01881)
D(LOG(M1))
-0.032337
(0.02647)
2 Cointegrating Equation(s):
Log likelihood
914.4749
Normalized cointegrating coefficients (standard error in parentheses)
LOG(KLCI)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
2.137505
-0.584987
(0.56514)
(0.50758)
0.000000
1.000000
-0.465072
0.389435
(0.15877)
(0.14260)
Adjustment coefficients (standard error in parentheses)
D(LOG(KLCI))
-0.342658
-1.393038
(0.13013)
(0.48131)
D(LOG(CPI))
0.000275
-0.023933
(0.01620)
(0.05992)
D(LOG(ER))
-0.080465
-0.239929
(0.04515)
(0.16700)
D(LOG(GDP))
0.114961
0.183179
(0.07823)
(0.28934)
D(LOG(IR))
0.105018
0.090097
(0.04036)
(0.14928)
D(LOG(M1))
-0.032545
0.010466
(0.05716)
(0.21141)
3 Cointegrating Equation(s):
Log likelihood
924.2863
Normalized cointegrating coefficients (standard error in parentheses)
LOG(KLCI)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.659298
(0.39511)
0.000000
1.000000
0.000000
0.118707
(0.06054)
0.000000
0.000000
1.000000
-0.582121
(0.25590)
Adjustment coefficients (standard error in parentheses)
292
D(LOG(KLCI))
D(LOG(CPI))
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
-0.329756
(0.13050)
-0.007350
(0.01409)
-0.084233
(0.04536)
0.103040
(0.07783)
0.111079
(0.04017)
-0.036163
(0.05751)
4 Cointegrating Equation(s):
-0.847907
(0.86364)
-0.346108
(0.09325)
-0.399095
(0.30016)
-0.320496
(0.51510)
0.346186
(0.26587)
-0.142388
(0.38063)
-0.008411
(0.17209)
-0.033291
(0.01858)
-0.082648
(0.05981)
0.090172
(0.10264)
0.218352
(0.05298)
-0.095787
(0.07584)
Log likelihood
931.5467
Normalized cointegrating coefficients (standard error in parentheses)
LOG(KLCI)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
Adjustment coefficients (standard error in parentheses)
D(LOG(KLCI))
-0.285238
-1.088693
0.069646
(0.13464)
(0.87846)
(0.18320)
D(LOG(CPI))
-0.002643
-0.371565
-0.025038
(0.01455)
(0.09490)
(0.01979)
D(LOG(ER))
-0.109932
-0.260090
-0.127710
(0.04571)
(0.29826)
(0.06220)
D(LOG(GDP))
0.061113
-0.093724
0.016657
(0.07873)
(0.51368)
(0.10713)
D(LOG(IR))
0.135184
0.215808
0.260617
(0.04031)
(0.26298)
(0.05484)
D(LOG(M1))
-0.038006
-0.132421
-0.099019
(0.06010)
(0.39210)
(0.08177)
5 Cointegrating Equation(s):
Log likelihood
LOG(IR)
-0.506907
(0.30600)
-0.028623
(0.03792)
-0.566363
(0.18009)
-0.331287
(0.16432)
LOG(M1)
-0.856683
(0.09843)
-0.221459
(0.01220)
0.011472
(0.05793)
-0.836000
(0.05286)
-0.087195
(0.23752)
-0.002661
(0.02566)
-0.185250
(0.08064)
-0.235419
(0.13889)
0.109577
(0.07111)
0.005662
(0.10602)
932.7922
Normalized cointegrating coefficients (standard error in parentheses)
LOG(KLCI)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
LOG(IR)
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
293
LOG(M1)
-0.734739
(0.05107)
-0.214573
(0.00481)
0.147719
(0.03484)
-0.756304
(0.02734)
0.240565
(0.06076)
Adjustment coefficients (standard error in parentheses)
D(LOG(KLCI))
-0.287691
-1.104940
(0.13493)
(0.88045)
D(LOG(CPI))
-0.002514
-0.370706
(0.01458)
(0.09516)
D(LOG(ER))
-0.106870
-0.239804
(0.04547)
(0.29669)
D(LOG(GDP))
0.062250
-0.086199
(0.07892)
(0.51496)
D(LOG(IR))
0.136748
0.226171
(0.04031)
(0.26302)
D(LOG(M1))
-0.035502
-0.115838
(0.06007)
(0.39200)
0.107413
(0.23962)
-0.027035
(0.02590)
-0.174863
(0.08075)
-0.000835
(0.14015)
0.236530
(0.07158)
-0.137565
(0.10669)
-0.099750
(0.24288)
-0.001997
(0.02625)
-0.169575
(0.08184)
-0.229604
(0.14206)
0.117584
(0.07256)
0.018476
(0.10814)
The Stock Exchange of Thailand (SET)
Date: 08/09/15 Time: 02:12
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Trend assumption: Linear deterministic trend
Series: LOG(SET) LOG(CPI) LOG(ER) LOG(GDP) LOG(IR) LOG(M1)
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
At most 2
At most 3
At most 4
At most 5
0.560292
0.385248
0.239113
0.093348
0.060550
0.036098
103.1630
55.50768
27.28856
11.43891
5.755104
2.132389
95.75366
69.81889
47.85613
29.79707
15.49471
3.841466
0.0140
0.3980
0.8431
0.9496
0.7242
0.1442
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
At most 2
At most 3
At most 4
At most 5
0.560292
0.385248
0.239113
0.093348
0.060550
0.036098
47.65535
28.21912
15.84965
5.683810
3.622715
2.132389
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
0.0058
0.2036
0.6785
0.9884
0.8969
0.1442
294
0.193850
(0.24647)
0.025522
(0.02664)
0.161092
(0.08305)
0.026988
(0.14415)
-0.276886
(0.07363)
0.048012
(0.10973)
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
LOG(SET)
-5.464038
1.842038
-0.190442
-2.300021
-4.501184
-0.594532
LOG(CPI)
-51.53124
36.20094
-64.67244
6.752542
1.100414
-18.59778
LOG(ER)
9.971474
6.429228
-16.31365
-4.254198
-11.11257
4.030529
LOG(GDP)
29.20875
28.54691
21.93120
-2.167181
0.729560
5.365805
LOG(IR)
10.46420
-5.672948
8.159675
6.436912
-7.498118
0.196587
LOG(M1)
14.08796
-24.98994
5.873685
-0.962262
0.848328
3.153515
-0.024939
-0.002246
-0.005657
-0.003671
0.006568
0.007448
-0.010150
0.002609
0.003972
-0.004449
-0.001873
-0.001065
-0.010011
-0.001643
0.001444
2.93E-05
-0.006618
-0.001192
0.010344
-1.58E-06
-3.74E-05
-0.002000
-0.000536
0.004135
Log likelihood
821.4573
LOG(IR)
-1.915103
(0.37192)
LOG(M1)
-2.578306
(0.55879)
LOG(IR)
-0.840582
(1.11840)
-0.113935
(0.10931)
LOG(M1)
7.559862
(1.79853)
-1.074985
(0.17579)
Unrestricted Adjustment Coefficients (alpha):
D(LOG(SET))
D(LOG(CPI))
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
0.038834
-0.000437
-0.011827
-0.009912
-0.005502
-0.010322
1 Cointegrating Equation(s):
Normalized cointegrating coefficients (standard error in parentheses)
LOG(SET)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
9.430982
-1.824928
-5.345634
(1.84258)
(0.54227)
(1.01731)
Adjustment coefficients (standard error in parentheses)
D(LOG(SET))
-0.212188
(0.06359)
D(LOG(CPI))
0.002386
(0.00666)
D(LOG(ER))
0.064624
(0.01659)
D(LOG(GDP))
0.054161
(0.01567)
D(LOG(IR))
0.030061
(0.02002)
D(LOG(M1))
0.056401
(0.02105)
2 Cointegrating Equation(s):
Log likelihood
835.5668
Normalized cointegrating coefficients (standard error in parentheses)
LOG(SET)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
-6.728978
-24.57641
(2.25744)
(4.10374)
0.000000
1.000000
0.519994
2.039107
(0.22064)
(0.40110)
Adjustment coefficients (standard error in parentheses)
295
0.005034
0.000357
-0.002547
0.002329
-0.001297
0.002766
D(LOG(SET))
D(LOG(CPI))
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
-0.258127
(0.06395)
-0.001752
(0.00678)
0.054204
(0.01689)
0.047400
(0.01626)
0.042159
(0.02043)
0.070120
(0.02137)
3 Cointegrating Equation(s):
-2.903969
(0.69842)
-0.058812
(0.07406)
0.404679
(0.18445)
0.377914
(0.17762)
0.521267
(0.22317)
0.801545
(0.23334)
Log likelihood
843.4916
Normalized cointegrating coefficients (standard error in parentheses)
LOG(SET)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
38.00621
(7.55268)
0.000000
1.000000
0.000000
-2.797076
(0.50850)
0.000000
0.000000
1.000000
9.300466
(1.61525)
Adjustment coefficients (standard error in parentheses)
D(LOG(SET))
-0.256194
-2.247560
(0.06344)
(0.99268)
D(LOG(CPI))
-0.002249
-0.227574
(0.00644)
(0.10079)
D(LOG(ER))
0.053447
0.147777
(0.01658)
(0.25948)
D(LOG(GDP))
0.048247
0.665656
(0.01586)
(0.24819)
D(LOG(IR))
0.042516
0.642427
(0.02039)
(0.31899)
D(LOG(M1))
0.070323
0.870450
(0.02136)
(0.33419)
4 Cointegrating Equation(s):
Log likelihood
LOG(IR)
-0.575723
(2.03256)
-0.134403
(0.13685)
0.039361
(0.43469)
LOG(M1)
-18.54695
(3.46173)
0.942464
(0.23307)
-3.879759
(0.74034)
LOG(IR)
-2.212262
(1.23020)
-0.013961
(0.09267)
-0.361115
(0.31274)
0.043060
(0.06057)
LOG(M1)
-0.961650
(0.33288)
-0.351730
(0.02508)
0.423523
(0.08462)
-0.462695
(0.01639)
0.392468
(0.22183)
-0.061366
(0.02252)
-0.219110
(0.05798)
-0.049855
(0.05546)
0.017931
(0.07128)
-0.037660
(0.07468)
846.3335
Normalized cointegrating coefficients (standard error in parentheses)
LOG(SET)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
Adjustment coefficients (standard error in parentheses)
D(LOG(SET))
-0.233170
-2.315157
0.435055
(0.06773)
(0.98717)
(0.22482)
D(LOG(CPI))
0.001531
-0.238671
-0.054375
296
0.221442
(0.50610)
-0.016087
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
(0.00678)
0.050126
(0.01781)
0.048180
(0.01708)
0.057738
(0.02116)
0.073066
(0.02297)
5 Cointegrating Equation(s):
(0.09886)
0.157528
(0.25955)
0.665853
(0.24888)
0.597736
(0.30846)
0.862399
(0.33478)
(0.02251)
-0.225253
(0.05911)
-0.049980
(0.05668)
0.046086
(0.07025)
-0.032588
(0.07624)
Log likelihood
848.1449
Normalized cointegrating coefficients (standard error in parentheses)
LOG(SET)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
(0.05068)
-0.422963
(0.13307)
-0.491946
(0.12760)
5.74E-05
(0.15814)
-0.109658
(0.17164)
LOG(IR)
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.320106
(0.25309)
-0.054357
(0.02558)
-0.224837
(0.06715)
-0.027759
(0.06405)
0.052042
(0.07978)
-0.078537
(0.08553)
0.228989
(0.50159)
-0.016088
(0.05069)
-0.422991
(0.13308)
-0.493405
(0.12693)
-0.000334
(0.15812)
-0.106641
(0.16950)
Adjustment coefficients (standard error in parentheses)
D(LOG(SET))
-0.279730
-2.303775
(0.08289)
(0.97832)
D(LOG(CPI))
0.001538
-0.238673
(0.00838)
(0.09887)
D(LOG(ER))
0.050295
0.157487
(0.02199)
(0.25957)
D(LOG(GDP))
0.057180
0.663653
(0.02098)
(0.24758)
D(LOG(IR))
0.060150
0.597146
(0.02613)
(0.30840)
D(LOG(M1))
0.054454
0.866949
(0.02801)
(0.33059)
LOG(M1)
-1.162522
(0.18800)
-0.352997
(0.02660)
0.390734
(0.05320)
-0.458786
(0.01460)
-0.090799
(0.13468)
0.323025
(0.18902)
0.018899
(0.01910)
-0.049682
(0.05015)
-0.104024
(0.04783)
-0.148699
(0.05958)
-0.197638
(0.06387)
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Date: 08/09/15 Time: 02:12
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Trend assumption: Linear deterministic trend
Series: LOG(IDX) LOG(CPI) LOG(ER) LOG(GDP) LOG(IR) LOG(M1)
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
297
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
At most 2
At most 3
At most 4
At most 5
0.524500
0.422094
0.228580
0.176412
0.140929
0.033891
112.0398
68.92330
37.11932
22.06705
10.81013
1.999736
95.75366
69.81889
47.85613
29.79707
15.49471
3.841466
0.0024
0.0588
0.3419
0.2948
0.2234
0.1573
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
At most 2
At most 3
At most 4
At most 5
0.524500
0.422094
0.228580
0.176412
0.140929
0.033891
43.11653
31.80398
15.05227
11.25692
8.810393
1.999736
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
0.0221
0.0866
0.7441
0.6217
0.3023
0.1573
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
LOG(IDX)
-4.650724
-1.114440
-1.516938
-0.887393
4.008247
-1.675377
LOG(CPI)
12.35682
-0.554456
16.64716
-13.36854
10.15606
6.878723
LOG(ER)
2.610118
8.633164
5.441610
5.478480
12.66768
-8.167148
LOG(GDP)
-2.639071
-2.219084
40.61217
-44.24656
-22.37393
-37.09360
LOG(IR)
0.510666
-15.82640
-9.200762
1.008605
3.094105
-4.139394
LOG(M1)
2.392304
-0.628334
-25.17966
23.87399
-1.965050
11.94684
-0.016747
0.002415
0.015926
0.003382
0.013986
1.73E-05
0.032735
0.002060
-0.010183
-0.002467
-0.000974
0.003265
-0.001546
0.004199
-0.005657
0.000262
0.003242
-0.004837
0.001404
-6.91E-06
-0.012277
0.002634
0.002160
0.004987
Log likelihood
788.5766
LOG(IR)
-0.109804
(0.51178)
LOG(M1)
-0.514394
(1.06245)
Unrestricted Adjustment Coefficients (alpha):
D(LOG(IDX))
D(LOG(CPI))
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
0.052329
-0.005198
-0.018686
-0.001136
-0.007474
-0.008568
1 Cointegrating Equation(s):
Normalized cointegrating coefficients (standard error in parentheses)
LOG(IDX)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
-2.656966
-0.561228
0.567454
(0.77782)
(0.54060)
(2.14097)
298
0.000424
-0.000175
0.002361
0.003011
-0.000817
0.003882
Adjustment coefficients (standard error in parentheses)
D(LOG(IDX))
-0.243365
(0.05834)
D(LOG(CPI))
0.024174
(0.00826)
D(LOG(ER))
0.086906
(0.03472)
D(LOG(GDP))
0.005285
(0.01269)
D(LOG(IR))
0.034759
(0.01652)
D(LOG(M1))
0.039847
(0.01937)
2 Cointegrating Equation(s):
Log likelihood
804.4786
Normalized cointegrating coefficients (standard error in parentheses)
LOG(IDX)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
-6.613372
1.766658
(2.23783)
(8.62207)
0.000000
1.000000
-2.277840
0.451343
(0.85754)
(3.30399)
LOG(IR)
11.94411
(2.15744)
4.536722
(0.82673)
LOG(M1)
0.393759
(3.38715)
0.341801
(1.29796)
LOG(IR)
-1.272236
(1.01136)
-0.015378
(0.30262)
-1.998428
(0.31934)
LOG(M1)
-5.613076
(1.71559)
-1.727130
(0.51333)
-0.908286
(0.54170)
Adjustment coefficients (standard error in parentheses)
D(LOG(IDX))
-0.224702
0.655900
(0.05891)
(0.15237)
D(LOG(CPI))
0.021482
-0.065569
(0.00834)
(0.02156)
D(LOG(ER))
0.069157
-0.239736
(0.03404)
(0.08804)
D(LOG(GDP))
0.001516
-0.015917
(0.01284)
(0.03322)
D(LOG(IR))
0.019173
-0.100107
(0.01411)
(0.03648)
D(LOG(M1))
0.039828
-0.105883
(0.01991)
(0.05151)
3 Cointegrating Equation(s):
Log likelihood
812.0048
Normalized cointegrating coefficients (standard error in parentheses)
LOG(IDX)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
8.866054
(4.33843)
0.000000
1.000000
0.000000
2.896584
(1.29813)
0.000000
0.000000
1.000000
1.073491
(1.36986)
Adjustment coefficients (standard error in parentheses)
D(LOG(IDX))
-0.274359
1.200847
(0.05727)
(0.23675)
D(LOG(CPI))
0.018358
-0.031281
(0.00862)
(0.03564)
D(LOG(ER))
0.084604
-0.409251
0.170133
(0.12025)
0.018493
(0.01810)
0.033306
299
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
(0.03497)
0.005259
(0.01336)
0.020650
(0.01478)
0.034875
(0.02076)
4 Cointegrating Equation(s):
(0.14456)
-0.056988
(0.05523)
-0.116324
(0.06110)
-0.051523
(0.08583)
(0.07342)
0.012805
(0.02805)
0.095931
(0.03103)
-0.004445
(0.04359)
Log likelihood
817.6332
Normalized cointegrating coefficients (standard error in parentheses)
LOG(IDX)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
Adjustment coefficients (standard error in parentheses)
D(LOG(IDX))
-0.272986
1.221521
0.161661
(0.05815)
(0.28162)
(0.13551)
D(LOG(CPI))
0.014632
-0.087415
0.041497
(0.00822)
(0.03979)
(0.01915)
D(LOG(ER))
0.089624
-0.333625
0.002315
(0.03528)
(0.17085)
(0.08221)
D(LOG(GDP))
0.005026
-0.060495
0.014242
(0.01357)
(0.06570)
(0.03162)
D(LOG(IR))
0.017774
-0.159660
0.113691
(0.01483)
(0.07181)
(0.03455)
D(LOG(M1))
0.039167
0.013143
-0.030945
(0.02080)
(0.10071)
(0.04846)
5 Cointegrating Equation(s):
Log likelihood
LOG(IR)
25.35539
(17.5664)
8.684001
(5.76232)
1.225613
(2.03361)
-3.003323
(1.91881)
1.296936
(0.68661)
-0.093784
(0.09701)
-0.149270
(0.41653)
-0.116309
(0.16019)
-0.194305
(0.17508)
0.369217
(0.24553)
822.0384
Normalized cointegrating coefficients (standard error in parentheses)
LOG(IDX)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
LOG(IR)
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.179445
(0.19813)
0.041410
(0.02800)
1.265525
(0.73245)
-0.093629
(0.10350)
Adjustment coefficients (standard error in parentheses)
D(LOG(IDX))
-0.267359
1.235779
(0.07398)
(0.30450)
D(LOG(CPI))
0.014604
-0.087485
(0.01045)
(0.04303)
LOG(M1)
-3.658282
(4.91490)
-1.088489
(1.61224)
-0.671602
(0.56898)
-0.220481
(0.53686)
300
LOG(M1)
-1.086772
(0.14651)
-0.207769
(0.07987)
-0.547302
(0.14206)
-0.525074
(0.03697)
-0.101419
(0.09065)
-0.006632
(0.21226)
-0.055618
(0.02999)
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
0.040415
(0.04345)
0.015585
(0.01709)
0.026431
(0.01876)
0.059155
(0.02606)
-0.458311
(0.17885)
-0.033739
(0.07035)
-0.137725
(0.07723)
0.063787
(0.10727)
-0.153206
(0.11637)
0.047615
(0.04578)
0.141050
(0.05025)
0.032223
(0.06980)
0.125414
(0.43021)
-0.175253
(0.16923)
-0.242628
(0.18576)
0.257647
(0.25802)
The Philippine Stock Exchange (PSE)
Date: 08/09/15 Time: 02:11
Sample (adjusted): 2000Q3 2014Q4
Included observations: 58 after adjustments
Trend assumption: Linear deterministic trend
Series: LOG(MSE) LOG(CPI) LOG(ER) LOG(GDP) LOG(IR) LOG(M1)
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None
At most 1
At most 2
At most 3
At most 4
At most 5
0.394988
0.332168
0.221464
0.151416
0.052108
0.004734
79.98270
50.83728
27.42157
12.90186
3.379081
0.275226
95.75366
69.81889
47.85613
29.79707
15.49471
3.841466
0.3652
0.6010
0.8378
0.8960
0.9471
0.5998
Trace test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None
At most 1
At most 2
At most 3
At most 4
At most 5
0.394988
0.332168
0.221464
0.151416
0.052108
0.004734
29.14542
23.41571
14.51971
9.522782
3.103856
0.275226
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
0.4814
0.4989
0.7851
0.7881
0.9396
0.5998
Max-eigenvalue test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
301
-0.211595
(0.12468)
-0.022988
(0.04904)
-0.206243
(0.05383)
-0.024144
(0.07477)
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
LOG(MSE)
6.950638
-2.833777
2.199073
5.767564
-1.429136
0.253866
LOG(CPI)
39.16000
10.40076
-34.58244
25.10009
25.98857
-16.40691
LOG(ER)
21.70992
-1.248406
1.733964
1.208665
-9.656320
-3.261344
LOG(GDP)
-66.47623
43.30269
-6.896228
-10.37417
-33.57581
5.498714
LOG(IR)
-2.836651
6.059160
9.856127
-0.335462
3.125599
2.764893
LOG(M1)
7.046430
-14.57277
15.92050
-10.29520
4.856210
5.760191
0.023200
-0.000635
-0.009007
-0.001866
-0.019151
0.011103
-0.005195
0.002089
-0.004871
-0.001279
-0.003858
-0.009365
-0.024448
1.80E-05
0.001545
0.001297
-0.003357
0.004927
-0.002858
-0.000563
0.001486
-0.000537
-0.007425
-0.001400
Log likelihood
837.4983
LOG(IR)
-0.408114
(0.31341)
LOG(M1)
1.013782
(0.63725)
LOG(IR)
-1.455725
(0.50112)
0.185944
(0.09197)
LOG(M1)
3.513853
(0.93889)
-0.443746
(0.17231)
Unrestricted Adjustment Coefficients (alpha):
D(LOG(MSE))
D(LOG(CPI))
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
0.009601
-0.001640
-0.012108
0.002192
0.000932
-0.004293
1 Cointegrating Equation(s):
Normalized cointegrating coefficients (standard error in parentheses)
LOG(MSE)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
5.634015
3.123442
-9.564047
(1.52269)
(0.40002)
(1.54166)
Adjustment coefficients (standard error in parentheses)
D(LOG(MSE))
0.066736
(0.08649)
D(LOG(CPI))
-0.011402
(0.00581)
D(LOG(ER))
-0.084156
(0.02479)
D(LOG(GDP))
0.015233
(0.00697)
D(LOG(IR))
0.006477
(0.05326)
D(LOG(M1))
-0.029839
(0.03104)
2 Cointegrating Equation(s):
Log likelihood
849.2061
Normalized cointegrating coefficients (standard error in parentheses)
LOG(MSE)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
1.498872
-13.02577
(0.69401)
(2.48391)
0.000000
1.000000
0.288350
0.614433
(0.12737)
(0.45587)
Adjustment coefficients (standard error in parentheses)
D(LOG(MSE))
0.000994
0.617288
(0.09010)
(0.48634)
D(LOG(CPI))
-0.009604
-0.070841
(0.00624)
(0.03369)
302
0.002882
0.000135
9.31E-05
0.000200
-0.001727
0.000132
D(LOG(ER))
D(LOG(GDP))
D(LOG(IR))
D(LOG(M1))
-0.058632
(0.02500)
0.020520
(0.00726)
0.060746
(0.05380)
-0.061302
(0.03138)
3 Cointegrating Equation(s):
-0.567812
(0.13496)
0.066415
(0.03919)
-0.162694
(0.29040)
-0.052638
(0.16938)
Log likelihood
856.4660
Normalized cointegrating coefficients (standard error in parentheses)
LOG(MSE)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
-20.68919
(6.57427)
0.000000
1.000000
0.000000
-0.859842
(0.57593)
0.000000
0.000000
1.000000
5.112790
(3.02216)
LOG(IR)
-4.929062
(1.28798)
-0.482250
(0.11283)
2.317300
(0.59208)
LOG(M1)
4.788661
(2.47535)
-0.198501
(0.21685)
-0.850511
(1.13791)
LOG(IR)
1.292759
(0.70068)
-0.223671
(0.06988)
0.779740
(0.27684)
0.300728
(0.07872)
LOG(M1)
-0.519249
(0.30320)
-0.419097
(0.03024)
0.461199
(0.11979)
-0.256555
(0.03407)
Adjustment coefficients (standard error in parentheses)
D(LOG(MSE))
-0.010430
0.796948
0.170477
(0.09371)
(0.63820)
(0.26135)
D(LOG(CPI))
-0.005009
-0.143098
-0.031199
(0.00608)
(0.04140)
(0.01695)
D(LOG(ER))
-0.069344
-0.399358
-0.260058
(0.02549)
(0.17360)
(0.07109)
D(LOG(GDP))
0.017708
0.110637
0.047690
(0.00743)
(0.05062)
(0.02073)
D(LOG(IR))
0.052261
-0.029259
0.037448
(0.05590)
(0.38069)
(0.15590)
D(LOG(M1))
-0.081897
0.271234
-0.123301
(0.03101)
(0.21121)
(0.08650)
4 Cointegrating Equation(s):
Log likelihood
861.2273
Normalized cointegrating coefficients (standard error in parentheses)
LOG(MSE)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
1.000000
Adjustment coefficients (standard error in parentheses)
D(LOG(MSE))
-0.151434
0.183309
0.140928
(0.11148)
(0.67548)
(0.25062)
D(LOG(CPI))
-0.004905
-0.142647
-0.031177
(0.00755)
(0.04576)
(0.01698)
D(LOG(ER))
-0.060435
-0.360584
-0.258191
(0.03160)
(0.19147)
(0.07104)
D(LOG(GDP))
0.025186
0.143180
0.049257
303
0.655784
(0.92120)
0.066975
(0.06241)
0.432417
(0.26113)
-0.231110
D(LOG(IR))
D(LOG(M1))
(0.00906)
0.032898
(0.06930)
-0.053478
(0.03793)
5 Cointegrating Equation(s):
(0.05491)
-0.113525
(0.41990)
0.394913
(0.22985)
(0.02037)
0.033390
(0.15579)
-0.117345
(0.08528)
Log likelihood
862.7793
Normalized cointegrating coefficients (standard error in parentheses)
LOG(MSE)
LOG(CPI)
LOG(ER)
LOG(GDP)
1.000000
0.000000
0.000000
0.000000
(0.07488)
-0.829794
(0.57266)
0.779630
(0.31347)
LOG(IR)
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
Adjustment coefficients (standard error in parentheses)
D(LOG(MSE))
-0.147350
0.109039
0.168524
(0.11260)
(0.73788)
(0.27383)
D(LOG(CPI))
-0.004101
-0.157281
-0.025740
(0.00759)
(0.04976)
(0.01847)
D(LOG(ER))
-0.062559
-0.321965
-0.272540
(0.03187)
(0.20885)
(0.07751)
D(LOG(GDP))
0.025953
0.129227
0.054441
(0.00913)
(0.05982)
(0.02220)
D(LOG(IR))
0.043509
-0.306481
0.105085
(0.06928)
(0.45398)
(0.16848)
D(LOG(M1))
-0.051476
0.358519
-0.103822
(0.03829)
(0.25092)
(0.09312)
0.751738
(0.99785)
0.085881
(0.06729)
0.382524
(0.28244)
-0.213083
(0.08089)
-0.580505
(0.61392)
0.826649
(0.33932)
APPENDIX J: Granger Causality Tests
FTSE Bursa Malaysia (FBMKLCI)
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 08/17/15 Time: 02:20
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: D(KLCI)
304
LOG(M1)
-1.012705
(0.12445)
-0.333720
(0.01229)
0.163566
(0.04387)
-0.371345
(0.01424)
0.381708
(0.05565)
0.061399
(0.14123)
0.019636
(0.00952)
-0.064112
(0.03998)
-0.032238
(0.01145)
-0.178791
(0.08689)
-0.018884
(0.04803)
Excluded
Chi-sq
df
Prob.
D(CPI)
D(ER)
D(GDP)
D(IR)
D(M1)
12.48291
1.436204
5.223435
0.815615
6.824571
1
1
1
1
1
0.0004
0.2308
0.0223
0.3665
0.0090
All
23.23085
5
0.0003
Dependent variable: D(CPI)
Excluded
Chi-sq
df
Prob.
D(KLCI)
D(ER)
D(GDP)
D(IR)
D(M1)
0.076171
3.490883
0.090338
0.008050
0.109306
1
1
1
1
1
0.7826
0.0617
0.7637
0.9285
0.7409
All
4.749433
5
0.4472
Dependent variable: D(ER)
Excluded
Chi-sq
df
Prob.
D(KLCI)
D(CPI)
D(GDP)
D(IR)
D(M1)
0.157778
11.44820
0.121412
0.064738
4.905910
1
1
1
1
1
0.6912
0.0007
0.7275
0.7992
0.0268
All
18.95753
5
0.0020
Dependent variable: D(GDP)
Excluded
Chi-sq
df
Prob.
D(KLCI)
D(CPI)
D(ER)
D(IR)
D(M1)
0.273237
8.761001
0.355515
0.339399
0.802279
1
1
1
1
1
0.6012
0.0031
0.5510
0.5602
0.3704
All
14.35282
5
0.0135
Dependent variable: D(IR)
Excluded
Chi-sq
df
Prob.
D(KLCI)
D(CPI)
D(ER)
0.115092
3.855417
1.211120
1
1
1
0.7344
0.0496
0.2711
305
D(GDP)
D(M1)
0.001183
0.828385
1
1
0.9726
0.3627
All
9.649675
5
0.0858
Dependent variable: D(M1)
Excluded
Chi-sq
df
Prob.
D(KLCI)
D(CPI)
D(ER)
D(GDP)
D(IR)
2.665648
0.128861
0.990867
1.709212
0.221243
1
1
1
1
1
0.1025
0.7196
0.3195
0.1911
0.6381
All
4.058334
5
0.5410
The Stock Exchange of Thailand (SET)
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 08/17/15 Time: 02:21
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: D(SET)
Excluded
Chi-sq
df
Prob.
D(CPI)
D(ER)
D(GDP)
D(IR)
D(M1)
0.053870
0.144924
0.038961
5.397842
0.203434
1
1
1
1
1
0.8165
0.7034
0.8435
0.0202
0.6520
All
7.859864
5
0.1641
Dependent variable: D(CPI)
Excluded
Chi-sq
df
Prob.
D(SET)
D(ER)
D(GDP)
D(IR)
D(M1)
0.450309
3.233942
0.016930
0.517731
6.976734
1
1
1
1
1
0.5022
0.0721
0.8965
0.4718
0.0083
306
All
13.47561
5
0.0193
Dependent variable: D(ER)
Excluded
Chi-sq
df
Prob.
D(SET)
D(CPI)
D(GDP)
D(IR)
D(M1)
6.102985
0.344860
0.015035
0.067960
0.235740
1
1
1
1
1
0.0135
0.5570
0.9024
0.7943
0.6273
All
8.329173
5
0.1390
Dependent variable: D(GDP)
Excluded
Chi-sq
df
Prob.
D(SET)
D(CPI)
D(ER)
D(IR)
D(M1)
0.263766
0.010705
1.778836
0.038046
2.941312
1
1
1
1
1
0.6075
0.9176
0.1823
0.8454
0.0863
All
6.145390
5
0.2923
Dependent variable: D(IR)
Excluded
Chi-sq
df
Prob.
D(SET)
D(CPI)
D(ER)
D(GDP)
D(M1)
3.364205
7.152249
0.347537
0.086097
3.193928
1
1
1
1
1
0.0666
0.0075
0.5555
0.7692
0.0739
All
23.34756
5
0.0003
Dependent variable: D(M1)
Excluded
Chi-sq
df
Prob.
D(SET)
D(CPI)
D(ER)
D(GDP)
D(IR)
1.512163
9.198658
0.158304
2.025012
0.353012
1
1
1
1
1
0.2188
0.0024
0.6907
0.1547
0.5524
All
14.34352
5
0.0136
307
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 08/17/15 Time: 02:16
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: D(IDX)
Excluded
Chi-sq
df
Prob.
D(CPI)
D(ER)
D(GDP)
D(IR)
D(M1)
0.160582
0.132664
7.028954
0.256839
2.118664
1
1
1
1
1
0.6886
0.7157
0.0080
0.6123
0.1455
All
10.18188
5
0.0702
Dependent variable: D(CPI)
Excluded
Chi-sq
df
Prob.
D(IDX)
D(ER)
D(GDP)
D(IR)
D(M1)
2.653313
1.185715
0.663304
2.258555
0.278928
1
1
1
1
1
0.1033
0.2762
0.4154
0.1329
0.5974
All
10.70865
5
0.0575
Dependent variable: D(ER)
Excluded
Chi-sq
df
Prob.
D(IDX)
D(CPI)
D(GDP)
D(IR)
D(M1)
3.618141
0.353735
0.619984
0.801384
0.293968
1
1
1
1
1
0.0572
0.5520
0.4311
0.3707
0.5877
All
8.939305
5
0.1115
Dependent variable: D(GDP)
Excluded
Chi-sq
df
Prob.
D(IDX)
D(CPI)
D(ER)
D(IR)
0.632274
0.172583
0.087834
0.469568
1
1
1
1
0.4265
0.6778
0.7669
0.4932
308
D(M1)
0.090403
1
0.7637
All
2.321564
5
0.8031
Dependent variable: D(IR)
Excluded
Chi-sq
df
Prob.
D(IDX)
D(CPI)
D(ER)
D(GDP)
D(M1)
0.004320
0.114128
0.686830
0.037652
0.100476
1
1
1
1
1
0.9476
0.7355
0.4072
0.8461
0.7513
All
1.970327
5
0.8532
Dependent variable: D(M1)
Excluded
Chi-sq
df
Prob.
D(IDX)
D(CPI)
D(ER)
D(GDP)
D(IR)
4.067047
0.256640
0.042391
0.960367
0.271822
1
1
1
1
1
0.0437
0.6124
0.8369
0.3271
0.6021
All
9.927644
5
0.0773
The Philippine Stock Exchange (PSE)
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 08/17/15 Time: 02:21
Sample: 2000Q1 2014Q4
Included observations: 58
Dependent variable: D(MSE)
Excluded
Chi-sq
df
Prob.
D(CPI)
D(ER)
D(GDP)
D(IR)
D(M1)
3.195988
0.069615
0.005578
0.019628
0.453862
1
1
1
1
1
0.0738
0.7919
0.9405
0.8886
0.5005
All
3.394506
5
0.6394
309
Dependent variable: D(CPI)
Excluded
Chi-sq
df
Prob.
D(MSE)
D(ER)
D(GDP)
D(IR)
D(M1)
0.749799
3.092722
0.180536
0.241847
3.734553
1
1
1
1
1
0.3865
0.0786
0.6709
0.6229
0.0533
All
6.781388
5
0.2374
Dependent variable: D(ER)
Excluded
Chi-sq
df
Prob.
D(MSE)
D(CPI)
D(GDP)
D(IR)
D(M1)
0.607798
9.327403
0.037566
1.004392
3.592201
1
1
1
1
1
0.4356
0.0023
0.8463
0.3163
0.0581
All
11.24949
5
0.0467
Dependent variable: D(GDP)
Excluded
Chi-sq
df
Prob.
D(MSE)
D(CPI)
D(ER)
D(IR)
D(M1)
8.484791
1.845731
0.963608
1.196062
1.553609
1
1
1
1
1
0.0036
0.1743
0.3263
0.2741
0.2126
All
13.28197
5
0.0209
Dependent variable: D(IR)
Excluded
Chi-sq
df
Prob.
D(MSE)
D(CPI)
D(ER)
D(GDP)
D(M1)
1.927895
5.492956
5.110826
3.193319
0.014763
1
1
1
1
1
0.1650
0.0191
0.0238
0.0739
0.9033
All
13.32584
5
0.0205
df
Prob.
Dependent variable: D(M1)
Excluded
Chi-sq
310
D(MSE)
D(CPI)
D(ER)
D(GDP)
D(IR)
0.137385
0.024763
0.828999
0.166771
0.030097
1
1
1
1
1
0.7109
0.8750
0.3626
0.6830
0.8623
All
1.265372
5
0.9385
APPENDIX K: Variance Decomposition
FTSE Bursa Malaysia (FBMKLCI)
Varian
ce
Decom
position
of
KLCI:
Period
S.E.
CPI
ER
GDP
IR
M1
KLCI
1
2
3
4
5
6
7
8
9
10
0.624899
0.897602
1.053333
1.206485
1.375096
1.545235
1.700269
1.830135
1.936809
2.029342
0.000000
4.442400
7.762350
7.756893
7.306911
7.659336
8.770503
10.27163
11.59214
12.33614
0.000000
0.236227
1.251321
1.754412
1.777736
1.747459
1.745665
1.759649
1.776312
1.767028
0.000000
0.249426
0.700411
0.802556
0.802721
0.834958
0.910983
0.984122
1.035196
1.078617
0.000000
0.840920
1.268518
1.886315
2.268323
2.312430
2.289942
2.374731
2.559191
2.763331
0.000000
4.539218
5.661452
6.342476
6.894088
7.168248
7.146299
7.010168
6.869891
6.733055
100.0000
89.69181
83.35595
81.45735
80.95022
80.27757
79.13661
77.59971
76.16727
75.32183
The Stock Exchange of Thailand (SET)
Varian
ce
Decom
position
of SET:
Period
S.E.
CPI
ER
GDP
IR
M1
SET
1
0.785125
0.000000
0.000000
0.000000
0.000000
0.000000
100.0000
311
2
3
4
5
6
7
8
9
10
1.225581
1.534560
1.761004
1.946082
2.109496
2.251283
2.371633
2.479313
2.583648
2.272917
9.951370
17.17116
19.96773
20.15923
19.82437
19.58966
19.40275
19.14887
0.324451
0.808555
0.662929
1.715851
3.368694
4.295741
4.607700
4.697836
4.677638
5.844149
7.682945
6.724602
6.196467
6.683399
7.597037
8.630860
9.661923
10.51870
0.665269
0.980105
1.136182
1.484035
1.934361
2.209048
2.284690
2.303651
2.345160
2.193643
8.711119
13.21128
15.06870
16.35446
18.21586
20.34314
22.08638
23.48500
88.69957
71.86591
61.09385
55.56721
51.49985
47.85794
44.54396
41.84746
39.82463
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
Varian
ce
Decom
position
of IDX:
Period
S.E.
CPI
ER
GDP
IR
M1
IDX
1
2
3
4
5
6
7
8
9
10
0.943006
1.234363
1.409893
1.566303
1.727891
1.887340
2.032440
2.158012
2.264434
2.356305
0.000000
1.383350
1.979865
1.752370
1.675532
1.865358
2.272380
2.700790
2.956879
3.021906
0.000000
0.652153
1.388787
1.592533
1.811095
2.074036
2.389638
2.809554
3.414328
4.180969
0.000000
1.977485
11.47368
18.15365
19.91139
20.30410
20.41939
20.36613
20.23455
20.12561
0.000000
0.870425
1.724159
2.259125
2.303263
2.254157
2.393773
2.610999
2.721945
2.691737
0.000000
0.068381
0.869101
0.748195
1.002248
1.596519
2.179172
2.697928
3.091607
3.335199
100.0000
95.04821
82.56440
75.49413
73.29647
71.90583
70.34565
68.81460
67.58069
66.64458
The Philippine Stock Exchange (PSE)
Varian
ce
Decom
position
of MSE:
Period
S.E.
CPI
ER
GDP
IR
M1
MSE
1
2
3
4
5
0.683857
1.097985
1.410066
1.683030
1.939998
0.000000
1.967344
3.333321
4.059460
4.689291
0.000000
0.043433
0.465321
1.333894
2.510198
0.000000
0.003171
0.003487
0.062267
0.264780
0.000000
0.352930
0.469256
0.409415
0.364747
0.000000
0.893164
1.365036
1.199178
1.100617
100.0000
96.73996
94.36358
92.93579
91.07037
312
6
7
8
9
10
2.188009
2.429404
2.664078
2.890470
3.106860
5.386441
6.131203
6.870522
7.571759
8.221094
3.808583
5.080606
6.228821
7.206604
8.009596
0.544328
0.788029
0.944825
1.018071
1.032715
0.391699
0.479326
0.605214
0.750842
0.904375
1.401786
2.139467
3.213480
4.464039
5.736653
88.46716
85.38137
82.13714
78.98868
76.09557
APPENDIX L: Impulse Response Function
FTSE Bursa Malaysia (FBMKLCI)
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of KLCI to KLCI
Response of KLCI to CPI
Response of KLCI to ER
Response of KLCI to GDP
Response of KLCI to IR
Response of KLCI to M1
120
120
120
120
120
120
80
80
80
80
80
80
40
40
40
40
40
40
0
0
0
0
0
0
-40
-40
-40
-40
-40
-40
1
2
3
4
5
6
7
8
9
10
1
2
Response of CPI to KLCI
3
4
5
6
7
8
9
10
1
2
Response of CPI to CPI
3
4
5
6
7
8
9
10
1
2
Response of CPI to ER
3
4
5
6
7
8
9
10
1
2
Response of CPI to GDP
3
4
5
6
7
8
9
10
1
1.0
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
2
3
4
5
6
7
8
9
10
1
2
Response of ER to KLCI
3
4
5
6
7
8
9
10
1
2
Response of ER to CPI
3
4
5
6
7
8
9
10
1
2
Response of ER to ER
3
4
5
6
7
8
9
10
1
2
Response of ER to GDP
3
4
5
6
7
8
9
10
1
.10
.10
.10
.10
.05
.05
.05
.05
.05
.05
.00
.00
.00
.00
.00
.00
-.05
-.05
-.05
-.05
-.05
-.05
-.10
2
3
4
5
6
7
8
9
10
-.10
1
2
Response of GDP to KLCI
3
4
5
6
7
8
9
10
-.10
1
2
Response of GDP to CPI
3
4
5
6
7
8
9
10
-.10
1
2
Response of GDP to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
8,000
8,000
8,000
4,000
4,000
4,000
4,000
4,000
4,000
0
0
0
0
0
0
-4,000
-4,000
-4,000
-4,000
-4,000
-4,000
-8,000
3
4
5
6
7
8
9
10
-8,000
1
2
Response of IR to KLCI
3
4
5
6
7
8
9
10
-8,000
1
2
Response of IR to CPI
3
4
5
6
7
8
9
10
-8,000
1
2
Response of IR to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
.1
.1
.1
.0
.0
.0
.0
.0
.0
-.1
3
4
5
6
7
8
9
10
-.1
1
2
Response of M1 to KLCI
3
4
5
6
7
8
9
10
-.1
1
2
Response of M1 to CPI
3
4
5
6
7
8
9
10
-.1
1
2
Response of M1 to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
8,000
8,000
8,000
4,000
4,000
4,000
4,000
4,000
4,000
0
0
0
0
0
0
-4,000
-4,000
-4,000
-4,000
-4,000
-4,000
-8,000
3
4
5
6
7
8
9
10
-8,000
1
2
3
4
5
6
7
8
9
10
-8,000
1
2
3
4
5
6
7
8
9
10
-8,000
1
2
3
4
5
6
7
8
9
10
The Stock Exchange of Thailand (SET)
313
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
Response of M1 to M1
8,000
2
2
Response of M1 to IR
8,000
1
9
-.1
1
Response of M1 to GDP
8,000
-8,000
8
Response of IR to M1
.1
2
2
Response of IR to IR
.1
1
7
-8,000
1
Response of IR to GDP
.1
-.1
6
Response of GDP to M1
8,000
2
2
Response of GDP to IR
8,000
1
5
-.10
1
Response of GDP to GDP
8,000
-8,000
4
Response of ER to M1
.10
1
2
Response of ER to IR
.10
-.10
3
Response of CPI to M1
1.0
1
2
Response of CPI to IR
-8,000
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of BSE to BSE
Response of BSE to CPI
Response of BSE to ER
Response of BSE to GDP
Response of BSE to IR
Response of BSE to M1
80
80
80
80
80
80
40
40
40
40
40
40
0
0
0
0
0
0
-40
-40
-40
-40
-40
-40
-80
-80
1
2
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to BSE
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to CPI
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to ER
3
4
5
6
7
8
9
10
-80
1
2
Response of CPI to GDP
3
4
5
6
7
8
9
10
1
1.0
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-1.0
1
2
3
4
5
6
7
8
9
10
-1.0
1
2
Response of ER to BSE
3
4
5
6
7
8
9
10
-1.0
1
2
Response of ER to CPI
3
4
5
6
7
8
9
10
-1.0
1
2
Response of ER to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-1.0
3
4
5
6
7
8
9
10
-1.0
1
2
Response of GDP to BSE
3
4
5
6
7
8
9
10
-1.0
1
2
Response of GDP to CPI
3
4
5
6
7
8
9
10
-1.0
1
2
Response of GDP to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
30,000
30,000
30,000
30,000
30,000
20,000
20,000
20,000
20,000
20,000
10,000
10,000
10,000
10,000
10,000
0
0
0
0
0
0
-10,000
-10,000
-10,000
-10,000
-10,000
-10,000
-20,000
3
4
5
6
7
8
9
10
-20,000
1
2
Response of IR to BSE
3
4
5
6
7
8
9
10
-20,000
1
2
Response of IR to CPI
3
4
5
6
7
8
9
10
-20,000
1
2
Response of IR to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
.3
.3
.3
.3
.3
.2
.2
.2
.2
.2
.1
.1
.1
.1
.1
.0
.0
.0
.0
.0
.0
-.1
-.1
-.1
-.1
-.1
-.1
-.2
3
4
5
6
7
8
9
10
-.2
1
2
Response of M1 to BSE
3
4
5
6
7
8
9
10
-.2
1
2
Response of M1 to CPI
3
4
5
6
7
8
9
10
-.2
1
2
Response of M1 to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
40
40
40
40
20
20
20
20
20
0
0
0
0
0
0
-20
-20
-20
-20
-20
-20
-40
3
4
5
6
7
8
9
10
-40
1
2
3
4
5
6
7
8
9
10
-40
1
2
3
4
5
6
7
8
9
10
-40
1
2
3
4
5
6
7
8
9
10
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
-40
1
2
3
4
Indonesia Stock Exchange (Bursa Efek Indonesia, IDX)
314
3
Response of M1 to M1
40
2
2
Response of M1 to IR
20
1
10
-.2
1
Response of M1 to GDP
40
-40
9
Response of IR to M1
.1
2
2
Response of IR to IR
.2
1
8
-20,000
1
Response of IR to GDP
.3
-.2
7
Response of GDP to M1
10,000
2
2
Response of GDP to IR
20,000
1
6
-1.0
1
Response of GDP to GDP
30,000
-20,000
5
Response of ER to M1
1.0
2
2
Response of ER to IR
0.5
1
4
-1.0
1
Response of ER to GDP
1.0
-1.0
3
Response of CPI to M1
1.0
-1.0
2
Response of CPI to IR
5
6
7
8
9
10
1
2
3
4
5
6
7
8
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of IDX to IDX
Response of IDX to CPI
Response of IDX to ER
Response of IDX to GDP
Response of IDX to IR
Response of IDX to M1
300
300
300
300
300
300
200
200
200
200
200
200
100
100
100
100
100
100
0
0
0
0
0
-100
-100
1
2
3
4
5
6
7
8
9
10
-100
1
2
Response of CPI to IDX
3
4
5
6
7
8
9
10
-100
1
2
Response of CPI to CPI
3
4
5
6
7
8
9
10
0
-100
1
2
Response of CPI to ER
3
4
5
6
7
8
9
10
-100
1
2
Response of CPI to GDP
3
4
5
6
7
8
9
10
1
1.0
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
2
3
4
5
6
7
8
9
10
1
2
Response of ER to IDX
3
4
5
6
7
8
9
10
1
2
Response of ER to CPI
3
4
5
6
7
8
9
10
1
2
Response of ER to ER
3
4
5
6
7
8
9
10
1
2
Response of ER to GDP
3
4
5
6
7
8
9
10
1
800
800
800
800
800
400
400
400
400
400
0
0
0
0
0
0
-400
-400
-400
-400
-400
-400
-800
2
3
4
5
6
7
8
9
10
-800
1
2
Response of GDP to IDX
3
4
5
6
7
8
9
10
-800
1
2
Response of GDP to CPI
3
4
5
6
7
8
9
10
-800
1
2
Response of GDP to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
12,000
12,000
12,000
12,000
8,000
8,000
8,000
8,000
8,000
4,000
4,000
4,000
4,000
4,000
4,000
0
0
0
0
0
-4,000
3
4
5
6
7
8
9
10
-4,000
1
2
Response of IR to IDX
3
4
5
6
7
8
9
10
-4,000
1
2
Response of IR to CPI
3
4
5
6
7
8
9
10
2
Response of IR to ER
3
4
5
6
7
8
9
10
2
Response of IR to GDP
3
4
5
6
7
8
9
10
1
.8
.8
.8
.8
.4
.4
.4
.4
.4
.0
.0
.0
.0
.0
.0
-.4
-.4
-.4
-.4
-.4
-.4
-.8
3
4
5
6
7
8
9
10
-.8
1
2
Response of M1 to IDX
3
4
5
6
7
8
9
10
-.8
1
2
Response of M1 to CPI
3
4
5
6
7
8
9
10
-.8
1
2
Response of M1 to ER
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
30,000
30,000
30,000
30,000
20,000
20,000
20,000
20,000
20,000
10,000
10,000
10,000
10,000
10,000
10,000
0
0
0
0
0
-10,000
3
4
5
6
7
8
9
10
-10,000
1
2
3
4
5
6
7
8
9
10
-10,000
1
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
The Philippine Stock Exchange (PSE)
315
10
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
0
-10,000
1
3
Response of M1 to M1
30,000
2
2
Response of M1 to IR
20,000
1
10
-.8
1
Response of M1 to GDP
30,000
-10,000
9
Response of IR to M1
.8
2
2
Response of IR to IR
.4
1
8
-4,000
1
.8
-.8
7
0
-4,000
1
6
Response of GDP to M1
12,000
2
2
Response of GDP to IR
8,000
1
5
-800
1
Response of GDP to GDP
12,000
-4,000
4
Response of ER to M1
400
1
2
Response of ER to IR
800
-800
3
Response of CPI to M1
1.0
1
2
Response of CPI to IR
-10,000
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of KLCI to M1
Response of KLCI to IR
Response of KLCI to GDP
Response of KLCI to ER
Response of KLCI to CPI
Response of KLCI to KLCI
120
120
120
120
120
120
80
80
80
80
80
80
40
40
40
40
40
40
0
0
0
0
0
1
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
0
1
10
2
3
4
5
6
7
8
9
1
10
1.0
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
.10
.10
.10
.10
.10
.05
.05
.05
.05
.05
.05
.00
.00
.00
.00
.00
.00
-.05
-.05
-.05
-.05
-.05
-.05
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
1
10
8,000
8,000
8,000
8,000
8,000
4,000
4,000
4,000
4,000
4,000
0
0
0
0
0
0
-4,000
-4,000
-4,000
-4,000
-4,000
-4,000
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
1
10
.1
.1
.1
.1
.1
.0
.0
.0
.0
.0
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
1
10
8,000
8,000
8,000
8,000
8,000
4,000
4,000
4,000
4,000
4,000
0
0
0
0
0
0
-4,000
-4,000
-4,000
-4,000
-4,000
-4,000
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
316
9
10
1
2
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
-8,000
-8,000
-8,000
-8,000
-8,000
2
10
Response of M1 to M1
4,000
1
2
Response of M1 to IR
8,000
-8,000
9
-.1
1
10
Response of M1 to GDP
Response of M1 to ER
Response of M1 to CPI
Response of M1 to KLCI
-.1
-.1
-.1
-.1
2
8
Response of IR to M1
.0
1
2
Response of IR to IR
.1
-.1
7
-8,000
1
10
Response of IR to GDP
Response of IR to ER
Response of IR to CPI
Response of IR to KLCI
-8,000
-8,000
-8,000
-8,000
2
6
Response of GDP to M1
4,000
1
2
Response of GDP to IR
8,000
-8,000
5
-.10
1
10
Response of GDP to GDP
Response of GDP to ER
Response of GDP to CPI
Response of GDP to KLCI
-.10
-.10
-.10
-.10
1
4
Response of ER to M1
.10
-.10
2
Response of ER to IR
Response of ER to GDP
Response of ER to ER
Response of ER to CPI
Response of ER to KLCI
3
Response of CPI to M1
1.0
1
2
Response of CPI to IR
Response of CPI to GDP
Response of CPI to ER
Response of CPI to CPI
Response of CPI to KLCI
3
-40
-40
-40
-40
-40
-40
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8