Journal of Critical Reviews
ISSN- 2394-5125
Vol 7, Issue 6, 2020
Review Article
STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC
INFORMATION: EVIDENCE FROM 30 SELECTED COUNTRIES
1Pei-Tha
Gan*, 1Awadh Ahmed Mohammed Gamal, 1Norimah Rambeli, Shu-Ern Lim, 2Muzafar Shah
Habibullah, 3Zalina Zainal
1Department
of Economics, Faculty of Management and Economics, Universiti Pendidikan Sultan Idris (Sultan Idris
Education University), 35900 Tanjong Malim, Perak. Malaysia.
* Corresponding author email: gan.pt@fpe.upsi.edu.my
2Department of Economics, Faculty of Economics and Management, Universiti Putra Malaysia, 43400 UPM Serdang,
Selangor. Malaysia.
3Department of Economics and Agribusiness, School of Economics, Finance and Banking, Universiti Utara Malaysia 06010
Sintok Kedah Malaysia
Received: 26.02.2020
Revised: 23.03.2020
Accepted: 20.04.2020
Abstract
A notable attribute of empirical studies on stock market integration is that very few published papers rely on leading informationbased in macroeconomics. To overcome this deficiency, this study examines the integration of stock markets across national borders
with leading information in macroeconomics based on a sample of 30 countries. Using the heterogeneous panel cointegration and the
catch-up methods, the findings provide some implications; the leading macroeconomic indicators can serve as an ongoing source of
support for traders, as an attempt to optimize stock return from investments across national borders. Further, it can also serve as a
buffer for countries’ regulatory support systems by avoiding economic catastrophes arising from excessive stock return co-movement
across national borders, driven by macroeconomic uncertainty that eventually helps both policymakers and traders to achieve mutual
benefits and promote stable economic conditions.
Keywords Informationally efficient market ∙ Heterogeneous panel cointegration ∙ Leading macroeconomic indicators ∙ Stock market
integration ∙ The catch-up effect
© 2019 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
DOI: http://dx.doi.org/10.31838/jcr.07.06.102
INTRODUCTION
Stock market integration is a phenomenon in which the stock
markets across national borders are linked together closely.
International financial integration allows capital to flow freely
across national borders and this opens up opportunities for
stock market integration (Surugiu & Surugiu, 2015;
Vithessonthi & Kumarasinghe, 2016). Although integration may
help to diversify risk but it may also increase the transmission
of economic turbulences across countries (Devereux & Yu,
2014). Thus, a financial catastrophe in a foreign country can
rapidly trigger a catastrophe in the domestic country (Berg,
1999). On the other hand, the empirical evidence for publicly
available information in the context of an informationally
efficient market argues that stock returns are, to a certain
extent, predictable and have important and broad economic
implications (Ferson, 2018). This evidence may give rise to
domestic stock market instability and performance
deterioration when traders gain excessive returns by exploiting
benefits of stock market integration (e.g., the decreased cost of
equity capital, portfolio diversification, and reduced financing
constraints) that eventually and inevitably induce economic
catastrophe. However, a puzzle remains as to whether studies
in this area could enhance our knowledge of the relationship
between publicly available information on the domestic stock
market and stock market integration and improve the
correctness of economic theory.
A notable attribute of empirical studies on stock market
integration is that very few published papers rely on leading
information-based in macroeconomics (e.g., output, inflation
rate, interest rate and exchange rate). The most commonly used
measurements of integration are price-based and quantitybased measures. Using both measures, Park and Lee (2011) find
that the prices of the emerging Asian equity market have
become increasingly internationally integrated in the aftermath
of the 1997/1998 crisis. Meanwhile, regarding price-based
measures, Majid et al. (2008), suggest that the ASEAN-5 stock
markets are working towards greater integration particularly
in the post-1997 crisis. Based on cointegration and Generalized
Journal of critical reviews
Method of Moments (GMM) result, they reveal that the Asean-5
become more dependent among themselves or with the US and
Japan. Berger et al. (2011), using various analytical techniques,
find that developed and emerging markets, except for frontier
markets, offer an indication of increasing integration through
time. Donadelli and Paradiso (2014) claim that the level of
integration across emerging equity markets in the emerging
regions, namely Asia, Eastern Europe, and Latin America, is
higher than in the global emerging region. Hillier and Loncan
(2019) suggest that governance quality is an important
stimulator of stock market integration in raising corporate
investment.
Regarding quantity-based measures, Kim et al. (2005) and
Wang and Moore (2008) employ the seemingly unrelated
estimation method and the linear regression method,
respectively, to examine the factors that influence the
integration process in Eurozone countries. They find that
financial liberalization, but not macroeconomic and monetary
factors, is a significant factor leading to stock market
integration in the Eurozone. The European Central Bank (2010)
reports that quantity-based indicators reveal that the degree of
integration in the Euro area equity markets is rising. Stavarek
et al. (2012) argue that quantity-based indicators seem to be
meaningful in assessing the financial integration of equity
markets. In line with those arguments, study by Lehkonen
(2015) also finds that the high degree of stock market
integration propagated the crisis across the global financial
markets at the beginning of the crisis, but it had little influence
during the crisis. He concludes that financial openness, in
addition to the institutional environment and global financial
uncertainty, may indeed promote market integration.
Pungulescu (2015) argues that the quantity-based measure of
financial integration may include factors regarding forego gains
from international diversification and overinvestment in
domestic stock that may promote international risk sharing and
domestic economic growth.
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STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
From other indirect measures of stock market integration, such
as described by Guvenen (2009), postulate a bilateral lead–lag
relationship between stock markets through macroeconomic
variables (e.g., inflation and volatility) using time-varying
correlation and covariance models. Using the method of panel
data analysis, Valdes et al. (2016) find that the factors in
promoting stock market integration in 18 stock markets in the
four regional blocs are individual market performance,
macroeconomic conditions, and agricultural trade. While
Sehgal et al. (2017) suggest that important drivers of market
integration are external position, fiscal position, governance,
stock market performance, and trade linkages. A recent study
by Tong et al. (2018), employs a visualized network measure of
global equity market integration and demonstrates that there
has been a propensity over time for stock markets in 57
countries to become more integrated internationally, even
during times of market stress. Although performance measures
of the stock market integration are not often disastrous, the
soundness of empirical evidence in supporting stock market
integration has reduced. Because the law of one price is not
empirically reliable (Miller, 1997; Pippenger & Phillips, 2008),
the feasibility of the price-based measure of stock market
integration may remain unclear. On the other hand, the
quantity-based measure of stock market integration is vague
when the capital asset pricing model is not empirically reliable
(Fama & French, 2004, 2006). Billio et al. (2017) argue that the
search for the precise form to address market integration must
carry on.
Inspired by the issue of macroeconomics indicators may affect
stock market integration, therefore the objective of this study is
to examine the integration of stock markets across national
borders with leading information in macroeconomics
(hereafter, leading macroeconomic indicators). The findings
from this study may contribute on the aspects of; (i) leading
macroeconomic indicators can serve as an ongoing source of
support for traders (e.g., investors, portfolio managers, and
investment agents and (ii) leading macroeconomic indicators
can serve as a buffer of the country’s regulatory support system
for policymakers by avoiding economic catastrophes arising
from excessive stock return co-movement across national
borders driven by macroeconomic uncertainty (e.g., output
uncertainty, inflation uncertainty, interest rate uncertainty, and
exchange rate uncertainty). In doing so, leading macroeconomic
indicators should help foster the convergence of stock markets
–– the stock market integration –– and help both policymakers
and traders to achieve mutual benefits and to promote stable
economic conditions. Because of limited data availability at the
country level, thirty selected countries — Argentina, Australia,
Brazil, Canada, China, Colombia, Czech Republic, Denmark,
Finland, Hong Kong, Indonesia, Ireland, Italy, Japan, Malaysia,
Mexico, New Zealand, Philippines, Poland, Russia, Singapore,
South Africa, South Korea, Spain, Sweden, Switzerland,
Thailand, Turkey, the UK, and the US — are included in this
study. The innovative feature of this paper is the use of a
combination of semi-strong informational efficiency and
heterogeneous panel cointegration approaches for the
measurement of stock market integration. Specifically, the
heterogeneous panel cointegration method examines the
response function of the stock market return underpinning the
semi-strong informational efficiency across national borders
(note that this paper assumes that the stock market return is a
proxy for the stock market). Additionally, this paper also
furthers to the catch-up method to support the soundness of
integration.
The following part of this paper is organized as follows; Section
2 elaborates the model specification and methodology and
Section 3 discusses the description of the data and empirical
results. Section 4 summarizes and concludes.
MODEL AND METHODOLOGY
In line with the hypothesis of this paper, the combination of
semi-strong informational efficiency and heterogeneous panel
cointegration approaches has tended to measure the
integration of stock markets across national borders with
Journal of critical reviews
leading information in macroeconomics. Specifically, across
national borders, the heterogeneous panel cointegration
method examines the response function of the stock market
return underpinning the assumptions that the stock market
return does not reflect all publicly available information. The
use of a panel cointegration procedure in determining the
condition of integration is a departure from the empirical
literature. Among other researchers, this technique is applied
by Eng and Habibullah (2006), Gan (2014) and Lu (2017).
Because integration may spur convergence, this paper also
furthers to the catch-up method.
Model specification: the semi-strong form
The semi-strong form efficiency appeared initially in the
seminal work by Roberts (1967), which implies that the prices
of security reflect all publicly available information. Roberts
(1967) first depicted the repercussions of the concept of an
informationally efficient market, a concept that the security
prices reflect all available information that, in turn, makes no
excess returns, and can characterize taxonomy of the known
information sets into three forms of the informational efficiency
of the security market: weak form efficiency, semi-strong
efficiency and strong form efficiency. This concept is furthered
by Fama (1970) to propagate his theory of investment (i.e., the
efficient market hypothesis). However, this informational
efficiency of security prices in more recent decades fails to
explain market anomalies (e.g., excess volatility and speculative
bubbles) that, in turn, lead to an erosion of the notion of
unpredictability of returns (Jegadeesh & Titman, 1993; Lasfer
et al., 2003; Mishkin & Eakins, 2015). On the other hand, Fama
(1981) and Chen et al. (1986) argue that the relationship
between stock market return and macroeconomic variables is
significant. Sharma and Wongbangpo (2002) and Nikkinen and
Sahlstrom (2004) find that the relationship between stock
market return and macroeconomic variables can exist not only
in the long-run but also in the short-run. Indeed, Samuelson
(1998), Shiller (2003) and Jung and Shiller (2005) assert that
the stock market is informational inefficiency at the macro-level
(i.e., aggregate-level) but informational efficiency at the microlevel (i.e., individual-level). To avoid repetitive presentations of
the shortcomings of the semi-strong form efficiency, the concept
of informationally efficient market still remains an important
part of modern finance (Shiller, 2013). Fama (1991) argues that
both the informational efficiency and the informational
inefficiency in the stock market are mutually compatible for
returns’ determination.
Following the convention of event analysis, the estimate of an
efficient market model (namely the semi-strong form) does not
differ significantly from its variants, except this estimate is
reassembled on the notion that an abnormal stock market
return should respond rapidly to changes in prime
macroeconomic activity through a set of leading
macroeconomic indicators: the output, the inflation rate, the
interest rate, and the exchange rate. Moreover, such an estimate
should not take place if the stock market operates efficiently. In
line with this notion, one can say that the market does not follow
a random walk (i.e., the market does not follow a non-stationary
process). The inputs for the semi-strong form of market
efficiency model of the stock market are given by the following
equation:
𝑠𝑡 = 𝛽1𝑡 𝑦𝑡 − 𝛽2𝑡 𝜋𝑡 − 𝛽3𝑡 𝑟𝑡 − 𝛽4𝑡 𝑒𝑡 + 𝜀𝑡
(1)
where, 𝑠 is stock market return, 𝑦 is the output, 𝜋 is the inflation
rate, 𝑟 is the interest rate, 𝑒 is the exchange rate, 𝑡 represents
time, β represents coefficients, and ε represents the error term.
All variables are in real terms, except 𝜋. From Eq. (1), 𝑠 depends
positively on and negatively on 𝜋, 𝑟, and 𝑒.
METHODOLOGY
The heterogeneous panel cointegration method
To relax the hypothesis of this paper, this subsection describes
the use of the heterogeneous panel cointegration method [also
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STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
knowns as the panel autoregressive distributed lag (ARDL)
method] to measure the integration of stock markets across
national borders with leading information in macroeconomics;
the heterogeneous panel cointegration method is proposed by
Pesaran et al. (1999). This method includes not only the longrun parameter estimates but also the averaged short-run
parameter estimates; although, frequently, only long-run
parameters are of interest. Following this method, Eq. (1) (i.e.,
the response function of the stock market return) can be
written in the panel error correction ARDL model as Eq. (2)
below.
𝑝−1
𝑞−1
∆𝑠𝑖𝑡 = 𝛽𝑖 + ∑𝑗=1 𝛼̂𝑖𝑗 ∆𝑠𝑖,𝑡−𝑗 + ∑𝑗=0 𝛼̂1𝑖𝑗 ∆𝑦𝑖,𝑡−𝑗 +
𝑞−1
𝑞−1
∑𝑗=0 𝛼̂2𝑖𝑗 ∆𝜋𝑖,𝑡−𝑗 + ∑𝑗=0 𝛼̂3𝑖𝑗 ∆𝑟𝑖,𝑡−𝑗 +
∑𝑞−1
̂4𝑖𝑗 ∆𝑒𝑖,𝑡−𝑗 + 𝛾̂𝑖 (𝑠𝑖,𝑡−1 − 𝛿̂1𝑖 𝑦𝑖𝑡 − 𝛿̂2𝑖 𝜋𝑖𝑡 − 𝛿̂3𝑖 𝑟𝑖𝑡 − 𝛿̂4𝑖 𝑒𝑖𝑡 ) +
𝑗=0 𝛼
𝑢𝑖 + 𝜀𝑖𝑡
(2)
where ∆ is the first difference, 𝑠 is stock market return, 𝑦 is the
output, 𝜋 is the inflation rate, 𝑟 is the interest rate, and 𝑒 is the
exchange rate. All variables are in real terms, except 𝜋. 𝛾̂𝑖 is the
error correction coefficient; 𝛿̂ is the long-run coefficient; 𝛼̂ is the
average short-run coefficient; 𝑢𝑖 is the group-specific effect; 𝑖 =
1,2,3, …, 𝑁 is the number of countries; and 𝑡 = 1, 2, 3, … , 𝑇 is the
number of times.
The mean group (MG) estimator and pooled mean group (PMG)
estimator can be described as follows:
1
1
1
𝛾̂𝑀𝐺 = ∑𝑁
𝛾̂ , 𝛿̂𝑀𝐺 = ∑𝑁
𝛿̂ , 𝛼̂𝑀𝐺 = ∑𝑁
𝛼̂
𝑁 𝑖=1 𝑖
𝑁 𝑖=1 𝑖
𝑁 𝑖=1 𝑖
(3)
1
1
𝛾̂𝑃𝑀𝐺 = ∑𝑁
𝛾̂ , 𝛿̂𝑃𝑀𝐺 = 𝛿̂𝑖 ∀𝑖 , 𝛼̂𝑃𝑀𝐺 = ∑𝑁
𝛼̂ ; ∀𝑖 = 1,2, … , 𝑁
𝑁 𝑖=1 𝑖
𝑁 𝑖=1 𝑖
(4)
The MG estimator relies on the estimation 𝑁 time series
regressions and the averaging coefficients; its estimates are the
unweighted average of the individual coefficients. The PMG
estimator, on the other hand, relies on both pooling and
averaging coefficients. This estimator restricts the long-run
coefficients to be identical across countries but allows shortrun coefficients to vary across countries.
From Eq. (2), the steady-state equilibrium for country 𝑖 can be
described as follows:
𝛽𝑖 + 𝛾̂𝑀𝐺 (𝑠𝑖∗ − 𝛿̂1𝑀𝐺 𝑦𝑖∗ − 𝛿̂2𝑀𝐺 𝜋𝑖∗ − 𝛿̂3𝑀𝐺 𝑟𝑖∗ − 𝛿̂4𝑀𝐺 𝑒𝑖∗ ) = 0
(5)
𝛽𝑖 + 𝛾̂𝑃𝑀𝐺 (𝑠𝑖∗ − 𝛿̂1𝑃𝑀𝐺 𝑦𝑖∗ − 𝛿̂2𝑃𝑀𝐺 𝜋𝑖∗ − 𝛿̂3𝑃𝑀𝐺 𝑟𝑖∗ − 𝛿̂4𝑃𝑀𝐺 𝑒𝑖∗ ) = 0
(6)
Provided that 𝛾̂ is the error correction coefficient on the
response function of the stock market return, the stock market
integration requires a stable long-run relationship between
stock market return and leading macroeconomic indicators
(i.e., 𝑦, 𝜋, 𝑟, and 𝑒) across national borders. 𝛾̂ is anticipated to be
negative and significantly different from zero. This coefficient
helps in determining the speed of adjustment (also known as
the speed of convergence) from disequilibrium to equilibrium
across countries. On the other hand, if the cointegrating vector
coefficients of the response function of the stock market return
are not statistically different from zero, no conclusion can be
drawn with respect to the stock markets across countries being
linked closely together. Otherwise, a short run interpretation is
appropriate.
The catch-up effect
The catch-up effect is a theory speculating that non-robust
countries may literally catch up –– to converge upon –– to a
more robust country (United Nations, 2000), and, thus, all
countries will, in time, converge in terms of the state variable.
This paper uses a catch-up method proposed by Sopek (2013)
to observe the difference of the state variable in two subsequent
periods. Note that this method is an additional method to obtain
more robust results to support the integration of stock markets.
The input for the trend in standard catching-up of weaker
countries is given by the following equation:
Journal of critical reviews
𝑐𝑟𝑖,𝑡 =
𝑘𝑖.𝑡
𝑘𝑡∗
−
𝑘𝑖,𝑡−1
(7)
∗
𝑘𝑡−1
The variables in Eq. (7) are described as follows: 𝑐𝑟𝑖 is the catchup rate of a country 𝑖, 𝑘𝑖 is the state variable of a country 𝑖, 𝑘 ∗ is
the state variable of a robust country, and 𝑡 is the number of
times. The 𝑐𝑟𝑖,𝑡 is an indicator that measures the pace of
diminishing disparity (i.e., catching-up) to a more robust
country. The disparity between the observed countries and an
anchor country is diminished in the case of positive difference
of the state variable, and vice versa. The disparity between the
observed countries and an anchor country may cease to exist
when the difference of the state variable becomes either zero or
near zero; the difference becoming either zero or near zero may
imply perfect convergence.
From the standard catching-up effect as described above, the
expression of the empirical model of the catch-up effect is as
follows:
𝑐𝑟𝑖,𝑡 =
𝑠𝑖.𝑡
𝑠𝑡𝑈𝑆
−
𝑠𝑖,𝑡−1
𝑈𝑆
𝑠𝑡−1
(8)
The variables in Eq. (8) are described as follows: 𝑐𝑟𝑖 is the catchup rate of a country 𝑖, 𝑠𝑖 is the stock market return of a country
𝑖, 𝑠 𝑈𝑆 is the stock market return of the US, and 𝑡 is the number
of times; 𝑠𝑖 and 𝑠 𝑈𝑆 are in real terms. The 𝑐𝑟𝑖,𝑡 is an indicator that
measures the pace of diminishing disparity (i.e., catching-up) to
the US. The disparity between the observed countries and the
US is diminished in the case of positive difference of the stock
market return, and vice versa. The disparity between the
observed countries and the US may cease to exist when the
difference of the stock market return becomes either zero or
near zero; the difference becoming either zero or near zero may
imply perfect convergence.
DATA AND EMPIRICAL RESULTS
Data description
Thirty selected countries — Argentina, Australia, Brazil,
Canada, China, Colombia, Czech Republic, Denmark, Finland,
Hong Kong, Indonesia, Ireland, Italy, Japan, Malaysia, Mexico,
New Zealand, Philippines, Poland, Russia, Singapore, South
Africa, South Korea, Spain, Sweden, Switzerland, Thailand,
Turkey, the UK, and the US — are included in this study. For
analysis purposes, this paper uses quarterly data from 1995
quarter one to 2018 quarter one. There are five variables: the
stock market return (𝑠𝑡 ), the output (𝑦𝑡 ), the inflation rate (𝜋𝑡 ),
the interest rate (𝑟𝑡 ) and the exchange rate (𝑒𝑡 ). All variables are
in log form, except 𝑠𝑡 , 𝜋𝑡 , and 𝑟𝑡 , and all variables are in real
terms, except 𝜋𝑡 . Note that this paper assumes that 𝑠𝑡 is a proxy
for the stock market. 𝑠𝑡 is calculated as the difference between
the nominal stock market return (𝑆𝑡 ) and 𝜋𝑡 . 𝑆𝑡 is constructed
(𝐶𝑆𝑃𝐼𝑐𝑢𝑟𝑟𝑒𝑛𝑡 −𝐶𝑆𝑃𝐼𝑏𝑎𝑠𝑒)100
with the following formula: 𝑆𝑡 =
, where
𝐶𝑆𝑃𝐼𝑏𝑎𝑠𝑒
𝐶𝑆𝑃𝐼 is the composite stock price index (in national currency
unit); the index is in log form. 𝜋𝑡 is constructed with the
(𝐶𝑃𝐼𝑐𝑢𝑟𝑟𝑒𝑛𝑡 −𝐶𝑃𝐼𝑏𝑎𝑠𝑒 )100
following formula: 𝜋𝑡 =
, where 𝐶𝑃𝐼 is the
𝐶𝑃𝐼𝑏𝑎𝑠𝑒
consumer price index. 𝑦𝑡 is obtained by dividing the nominal
gross domestic product (GDP) with the consumer price index
(CPI). 𝑟𝑡 is calculated as the difference between the nominal
interest rate (𝑅𝑡 ) and the 𝜋𝑡 ; 𝑅𝑡 is the money market rate
(MMR). 𝑒𝑡 is the real effective exchange rate (REER). Note that
the 𝐶𝑆𝑃𝐼, the 𝐶𝑃𝐼, the MMR, the nominal GDP, and the REER are
taken from various sources, including Datastream, the Bank for
International Settlements Statistics, and
International
Financial Statistics from International Monetary Fund.
RESULTS DISCUSSION
Three different panel unit root tests namely, Im et al. (2003),
Levin et al. (2002), and Maddala are used to assess the
stationarity of the dataset. In each test, the null hypothesis of a
unit root is tested against the alternative that the process is
stationary. In addition to the panel unit root tests, this study
uses the Pesaran’s (2004) cross-section dependence (CD) test
to examine whether the null hypothesis of cross-sectional
independence holds; failure to reject the null hypothesis
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STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
suggests the countries in our sample are not integrated entities,
and thus, a potential shock cannot be transmitted to other
countries. Additionally, we also uses the heterogeneous panel
causality test of Dumitrescu and Hurlin (2012) to determine
causal relations between stock market return (𝑠𝑡 ) and leading
macroeconomic indicators: output (𝑦𝑡 ), inflation rate (𝜋𝑡 ),
interest rate (𝑟𝑡 ), and exchange rate (𝑒𝑡 ). From Table 1, the
results reveal that stationarity can generally be assumed for all
variables, namely 𝑠𝑡 , 𝑦𝑡 , 𝜋𝑡 , 𝑟𝑡 , and 𝑒𝑡 ; in other words, these
variables are integrated of order zero, I(0). These results,
however, do not halt the analysis of the panel error correction
ARDL (i.e., the heterogeneous panel cointegration) because
variables are allowed to follow different orders of integration,
Tests
i.e., I(0) and/or I(1) (Pesaran and Shin, 1999). The results of the
Pesaran’s (2004) CD test, on the other hand, reveal that the null
hypothesis of cross-sectional independence is rejected. This
evidence suggests that the countries in our sample are
integrated entities, and, thus, a potential shock can be
transmitted to other countries. In the presence of crosssectional dependence, one can investigate the long-run
relationships among the variables that concern cross-sectional
dependency. Furthermore, the results of the heterogeneous
panel causality test suggest that the null hypothesis of no causal
relation between 𝑠𝑡 and each cause (i.e., 𝑦𝑡 , 𝜋𝑡 , 𝑟𝑡 , and 𝑒𝑡 ) can be
rejected at the 1% significance level.
Table 1 (a) Panel unit root tests; (b) Cross-section dependence test; (c) Heterogeneous panel causality test
𝑠𝑡
𝑦𝑡
𝜋𝑡
𝑟𝑡
𝑒𝑡
(a)
Levin et al. (2002)
Level
First Difference
Decision
-15.50 ** [2]
-29.26 ** [2]
I(0)
-3.95 ** [1]
-44.34 ** [2]
I(0)
-15.15 ** [2]
-41.84 ** [2]
I(0)
-2.91 ** [2]
-17.78 ** [2]
I(0)
-2.63 ** [1]
-17.97 ** [2]
I(0)
Im et al. (2003)
Level
First Difference
Decision
-17.92 ** [2]
-44.18 ** [2]
I(0)
-3.13 ** [2]
-59.78 ** [2]
I(0)
-20.13 ** [2]
-60.21 ** [2]
I(0)
-6.06 ** [5]
-38.08 ** [2]
I(0)
-1.80 * [2]
-23.78 ** [2]
I(0)
Maddala and Wu (1999)
Level
First Difference
Decision
413.01 ** [2]
983.89 ** [2]
I(0)
274.95 ** [1]
740.91 ** [2]
I(0)
487.63 ** [2]
756.92 ** [2]
I(0)
196.63 ** [2]
833.35 ** [2]
I(0)
92.33 ** [1]
637.22 ** [2]
I(0)
(b)
Pesaran (2004)
72.48 **
135.60 **
38.18 **
108.86**
6.93 **
(c)
Dumitrescu and Hurlin (2012)
Null hypothesis
Zbar-Statistic
p-value
𝑦𝑡 does not cause 𝑠𝑡
6.02 [2]
0.000 **
𝜋𝑡 does not cause 𝑠𝑡
17.20 [2]
0.000 **
𝑟𝑡 does not cause 𝑠𝑡
13.77 [2]
0.000 **
𝑒𝑡 does not cause 𝑠𝑡
6.61 [2]
0.000 **
Source Author’s calculations using the software package Eviews 10
Notes: * and ** indicate the rejection of the null hypothesis of a unit root at the 5% and 1% levels, respectively. The numbers within
brackets represent the lag length; the lag lengths are selected using the Schwarz information criterion. I(0) is integrated of order zero.
p-value is the probability.
We then examine the response function of the stock market
return for 30 selected countries by using the heterogeneous
panel cointegration method proposed by Pesaran et al. (1999).
Across national borders, the method enables us to test the
existence of a long-run relationship between stock market
return (𝑠𝑡 ) and its determinants, namely, output (𝑦𝑡 ), inflation
(𝜋𝑡 ), interest rate (𝑟𝑡 ), and exchange rate (𝑒𝑡 ). The Hausman
test is applied to examine whether the null hypothesis of longrun slope homogeneity holds; a failure to reject the null
hypothesis suggests that the PMG estimator is consistent and
efficient against the MG estimator. Additionally, although it is
often the case that only long-run coefficients are of interest via
the heterogeneous panel cointegration method, this method
can apprehend the relationship between 𝑠𝑡 and its
determinants (i.e., 𝑦𝑡 , 𝜋𝑡 , 𝑟𝑡 , and 𝑒𝑡 ) in the short-run.
Table 2 reports the results of the heterogeneous panel
cointegration tests (i.e., MG and PMG estimates), for the
response function of the stock market return; the tests use data
from 1995 quarter one to 2018 quarter one. The value of the
Hausman test statistic is 1.5 (p-value 0.826), which suggests
that the null hypothesis of long-run slope homogeneity holds
Journal of critical reviews
and that one can pool the data of all independent variables.
Comparing the MG and PMG estimates, imposing long-run
homogeneity has reduced the standard errors with a rapid
speed of adjustment towards zero; hence, the PMG estimates
are consistent and efficient against MG estimates. The error
correction coefficient term from the PMG model is negative (0.757) and statistically significant. This coefficient term shows
a high level of convergence to equilibrium, which indicates that
76 percent of any deviation from the long-run equilibrium is
restored within one quarter. Although the PMG results reveal
that all long-run coefficients are statistically significant, only the
signs of the long-run coefficients of 𝜋𝑡 (-1.223), 𝑟𝑡 (-0.029) and
𝑒𝑡 (-0.062) are correct, except 𝑦𝑡 (-1.012); however, the PMG
results reveal that the averaged short-run coefficients are
statistically significant and have a correct sign. Therefore, we
conclude that a long-run relationship exists between 𝑠𝑡 and its
determinants, namely 𝜋𝑡 , 𝑟𝑡 , and 𝑒𝑡 ; for the short run dynamics,
variables are influenced by the deviation from the long run
equilibrium. In general, we can conclude that leading
macroeconomic indicators play an important role in shaping
the integration of stock markets across national borders.
571
STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
Table 2 Panel error correction model estimations of the response function of the stock market return of 30 selected
countries.
ARDL
MG estimates
PMG estimates
Hausman test
(10, 11, 5, 5, 19)
Coef.
Std. err.
t-ratio
Coef.
Std. err.
t-ratio
H
p-value
Long-run coefficients
𝑦𝑡
𝜋𝑡
0.009
-1.548
1.528
0.213
0.01
-7.27 ***
-1.012
-1.223
0.164
0.044
-6.19 ***
-28.08 ***
𝑟𝑡
-0.240
0.178
-1.35
-0.029
0.008
-3.55 ***
𝑒𝑡
-0.324
2.726
-0.12
-0.062
0.032
-1.93 *
0.048
-18.00 ***
-0.757
0.033
-23.17 ***
10.31 (4)
5.037
2.05 **
4.793 (7)
2.516
1.91 *
1.50
0.826
Error-correction coefficient
𝐸𝐶
-0.867
Short-run coefficients a
𝑦𝑡
8.214 (10)
2.979
2.76 ***
𝜋𝑡
-0.751 (1)
0.280
-2.68 ***
-0.627 (1)
0.252
-2.48 **
𝑟𝑡
-0.569 (1)
0.264
-2.15 **
-0.448 (1)
0.243
-1.84 *
𝑒𝑡
-4.175 (10)
2.537
-1.65 *
-6.859 (2)
2.950
-2.33 **
Source Author’s calculation using the software package STATA 14
Notes: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. ( ) is the lag length; the lag lengths are
selected using the Schwarz’s Bayesian criterion. p-value is the probability. a Reporting only those variables (namely 𝑦, 𝜋, 𝑟 and 𝑒) that
are significant and that have the expected sign.
Furthermore, Table 3 reports only the individual countries’
results of the PMG estimates, because the individual countries’
results of the MG estimates are less appropriate (see the
Hausman test result). The empirical response function of the
stock market return is stable. The summary results of the tests
of the Ramsey regression specification error test (RESET) — the
functional form misspecification test — reveal that the
individual countries’ regression models are specified correctly
(by including 𝑦𝑡 , 𝜋𝑡 , 𝑟𝑡 and 𝑒𝑡 ), except for the Czech Republic and
Italy; the null hypothesis of the RESET test is no specification
error. The adjusted R-squared values suggest generally that the
model fits the data well; the adjusted R-squared reflects the
goodness of fit of the model. Overall, the results of Table 3
support the above-mentioned results' discussion of PMG
estimates, though there are a few unfavorable results arising
from the insignificant regression coefficient, incorrect
functional form, and low adjusted R-squared value; the
occurrence of unfavorable results is likely because of
insufficient observations (Stewart 1976).
Table 3 Individual countries’ results of the PMG estimates of the response function of the stock market return.
Countries
𝐸𝐶 coef.
𝑦𝑡 coef. a
𝜋𝑡 coef. a
𝑟𝑡 coef. a
𝑒𝑡 coef. a
Adj. 𝑅 2 Ramsey
RESET test
Argentina
-0.923
39.025 (10)
-0.951 (1)
-0.317 (2)
-55.449 (6)
0.712
2.263
[-9.38] ***
[1.90] *
[-2.14] **
[-3.70] ***
[-4.95] ***
(𝑝 = 0.14)
59.278 (11)
-62.133 (10)
[4.05] ***
[-6.11] ***
-14.328 (14)
[-1.83] *
-23.428 (18)
[-4.89] ***
Australia
-0.838
18.689 (1)
-1.329 (3)
-6.299 (16)
0.485
0.762
[8.07]* **
[1.75] *
[-1.75] *
[-1.87] *
(𝑝 = 0.39)
22.215 (4)
-5.373 (19)
[1.95] *
[-2.02] **
Brazil
-0.653
26.354 (10)
-1.865 (2)
-0.543 (4)
-11.957 (15)
0.605
0.689
[-5.63] ***
[2.39] **
[-2.76] ***
[-1.98] **
[-4.82] ***
(𝑝 = 0.42)
-0.875 (4)
-6.615 (17)
[-1.96] **
[-2.60] ***
-9.116 (19)
[-5.15] ***
Canada
-1.710
22.576 (4)
-1.159 (4)
-1.172 (4)
-11.820 (2)
0.699
0.116
[-8.60] ***
[3.74] ***
[-2.06] **
[-2.59] ***
[-2.99] ***
(p = 0.74)
25.723 (9)
-9.458 (5)
[3.78] ***
[-3.00] ***
-11.174 (13)
[-3.28] ***
-14.681 (16)
[-4.57] ***
China
-0.728
83.021 (8)
-2.708 (3)
-21.775 (4)
0.584
2.629
[-7.01] ***
[3.15] ***
[-2.46] **
[-2.24] **
(𝑝 = 0.13)
-18.495 (6)
[-2.13] **
Journal of critical reviews
572
STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
Colombia
-1.019
[-8.59] ***
20.861 (5)
[3.15] ***
16.439 (10)
[3.16] ***
-0.804 (2)
[-1.71] *
-0.555 (2)
[-1.73] *
Czech
Republic
-0.773
[-8.59] ***
8.568 (1)
[3.36] ***
5.748 (3)
[2.48] **
-1.575 (4)
[-2.54] **
-1.246 (2)
[-1.77] *
-1.033 (4)
[-2.11] **
Denmark
-0.590
[-5.77] ***
19.272 (3)
[4.72] ***
18.697 (4)
[3.34] ***
10.586 (5)
[1.75]*
-0.826 (1)
[-1.77] *
-1.512 (3)
[-2.64] ***
-2.243 (4)
[-4.19] ***
-0.823 (5)
[-1.80] *
Finland
-0.749
[-9.02] ***
-2.675 (1)
[-5.09] ***
-2.113 (2)
[-3.21] ***
Hong Kong
-0.993
[-8.33] ***
6.135 (1)
[2.38] **
4.672 (2)
[1.79] *
10.122 (7)
[4.38] ***
5.019 (8)
[2.06] **
7.602 (9)
[3.69] ***
20.893 (10)
[2.25] **
-0.894 (1)
[-3.30] ***
-0.601 (2)
[-2.02] **
-1.380 (3)
[-3.68] ***
-1.398 (4)
[-3.50] ***
-1.261 (5)
[-3.30] ***
-1.830 (1)
[-2.92] ***
-1.843 (2)
[-3.64] ***
Indonesia
-0.624
[-7.75] ***
-0.405 (1)
[-1.87] *
Ireland
-0.461
[-5.24] ***
18.769 (3)
[2.25] **
36.532 (5)
[3.87] ***
2.997 (4)
[2.58] ***
3.363 (5)
[2.48] **
Italy
-0.809
[-7.17] ***
6.757 (5)
[1.92] *
7.324 (8)
[1.74] *
-1.401 (2)
[-2.47] **
-1.175 (4)
[-1.77] *
-1.111 (1)
[-2.84] ***
-0.623 (2)
[-1.85] *
-0.943 (4)
[-2.50] **
Japan
-0.652
[-6.23] ***
43.765 (4)
[3.32] ***
35.019 (5)
[2.75] ***
56.309 (10)
[4.51] ***
58.991 (11)
[4.51] ***
-5.494 (1)
[-1.83] *
-5.165 (3)
[-1.73] *
-5.356 (1)
[-1.76] *
-6.205 (3)
[-2.06] **
Journal of critical reviews
-1.020 (1)
[-1.73] *
-21.249 (10)
[-2.52] **
-8.292 (3)
[-2.43] **
-3.993 (9)
[-1.82] *
-9.591 (12)
[-4.31] ***
-6.703 (17)
[-2.61] ***
-10.316 (18)
[-4.16] ***
-25.836 (2)
[-3.81] ***
-16.238 (8)
[-2.47] **
-16.265 (11)
[-2.27] **
-14.449 (19)
[-2.16] **
-47.998 (2)
[-2.66] ***
-35.736 (13)
[-2.22] **
-29.515 (16)
[-2.04] **
0.680
0.017
(𝑝 = 0.90)
0.579
3.558*
(𝑝 = 0.07))
0.231
0.004
(𝑝 = 0.95)
-26.279 (2)
[-2.29] **
-47.739 (5)
[-3.25] ***
-19.080 (10)
[-1.78] *
-19.489 (18)
[-1.90] *
0.678
2.093
(𝑝 = 0.11)
-12.588 (5)
[-1.81] *
-11.194 (18)
[-1.84] *
-0.097 (11)
[-2.63] ***
-0.108 (17)
[-3.27] ***
-28.442 (2)
[-3.45] ***
-18.392 (6)
[-2.01] **
-16.755 (10)
[-2.02] **
-39.747 (3)
[-2.58] ***
-94.040 (7)
[-4.78] ***
-46.821 (10)
[-2.36] **
-53.542 (11)
[-2.47] **
-21.955 (15)
[-1.66] *
-12.750 (6)
[-2.90] ***
-17.199 (11)
[-4.24] ***
-8.024 (15)
[-2.01] **
-11.536 (16)
[-2.79] ***
-6.742 (19)
[-1.81] *
0.904
0.024
(𝑝 = 0.88)
0.757
1.041
(𝑝 = 0.32)
0.416
0.016
(𝑝 = 0.90)
0.551
6.634**
(𝑝 = 0.02)
0.401
0.007
(𝑝 = 0.94)
573
STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
Malaysia
-0.905
[-9.50] ***
8.079 (10)
[2.11] **
-1.932 (1)
[-1.69] *
Mexico
-0.795
[-8.89] ***
17.231 (10)
[3.31] ***
-0.621 (1)
[-3.09] ***
-0.344 (1)
[-2.10] **
-0.290 (2)
[-1.68] *
New
Zealand
-0.852
[-9.62] ***
34.570 (2)
[5.15] ***
28.398 (3)
[4.37] ***
13.733 (4)
[2.17] **
23.595 (10)
[3.61] ***
-1.612 (2)
[-3.09] ***
-0.843 (4)
[-1.65] *
-1.322 (5)
[-4.04] ***
-0.971 (2)
[-2.22] **
Philippines
-0.650
[-6.11] ***
Poland
-0.417
[-5.45] ***
11.768 (6)
[1.87] *
9.520 (9)
[1.82] *
11.527 (10)
[2.51] **
13.671 (11)
[3.44] ***
-0.648 (3)
[-2.40] **
-0.989 (5)
[-3.75] ***
-0.561 (2)
[-3.39] ***
-0.362 (3)
[-2.32] **
Russia
-0.574
[-4.74] ***
26.724 (5)
[3.49] ***
-0.646 (1)
[-2.55] **
-0.241 (5)
[-2.26] **
Singapore
-0.721
[-6.92] ***
14.817 (10)
[2.61] ***
South Africa
-0.896
[-13.47] ***
25.092 (1)
[3.54] ***
-0.392 (1)
[-1.79] *
-0.483 (4)
[-2.33] **
South Korea
-0.889
[-7.59] ***
-1.290 (1)
[-1.89] *
Spain
-0.373
[-4.30] ***
30.424 (7)
[1.90] *
27.804 (10)
[2.52] **
18.415 (4)
[4.90] ***
-1.809 (2)
[-3.49] ***
Journal of critical reviews
0.688
1.944
(𝑝 = 0.18)
0.755
0.677
(𝑝 = 0.42)
0.739
2.580
(𝑝 = 0.10)
0.453
1.237
(𝑝 = 0.28)
0.713
0.025
(𝑝 = 0.88)
0.499
1.136
(𝑝 = 0.30)
0.528
1.460
(𝑝 = 0.24)
0.731
1.936
(𝑝 = 0.15)
-1.377 (1)
[-1.71] *
-10.679 (6)
[-2.25]**
-17.520 (9)
[-3.20]***
-18.160 (12)
[-2.63]***
-9.201 (14)
[-1.80]*
-7.716 (2)
[-3.95] ***
-7.263 (3)
[-3.67] ***
-12.051 (4)
[-4.98] ***
-7.680 (12)
[-3.86] ***
-4.128 (14)
[-1.91] *
-5.517 (1)
[-2.47] **
-9.508 (3)
[-3.75] ***
-5.907 (6)
[-2.36] **
-4.259 (9)
[-1.80] *
-4.321 (10)
[-1.91] *
-8.592 (16)
[-4.01] ***
-20.188 (19)
[-7.76] ***
-13.518 (4)
[-1.98] **
-8.903 (12)
[-1.88] *
-6.677 (1)
[-3.03] ***
-8.721 (4)
[-3.05] ***
-12.520 (6)
[-5.15] ***
-4.875 (10)
[-2.08] **
-12.013 (12)
[-5.75]***
-11.435 (4)
[-2.44]**
-17.551 (12)
[-3.84]***
-11.768 (14)
[-1.92]*
-13.354 (18)
[-2.17] **
-32.272 (8)
[-2.56] **
-22.567 (16)
[-1.82] *
-4.007 (1)
[-2.77] ***
-4.317 (4)
[-2.47] **
-3.717 (6)
[-2.34] **
-2.897 (12)
[-1.84] *
-4.391 (16)
[-2.98] ***
-8.019 (12)
[-1.66] *
0.706
2.522
(𝑝 = 0.13)
-1.510 (2)
[-3.17] ***
-48.455 (2)
[-3.57] ***
0.744
1.335
(𝑝 = 0.26)
574
STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
11.842 (7)
[2.38] **
6.906 (10)
[2.03] **
Sweden
-0.754
[-7.06] ***
Switzerland
-0.645
[-6.55] ***
Thailand
-0.880 (4)
[-1.76] *
8.082 (6)
[1.82 ]*
7.303 (9)
[1.88] *
8.228 (10)
[2.33] **
1.175 (7)
[2.29] **
-2.657 (1)
[-3.53] ***
-2.272 (5)
[-2.10] **
-1.569 (1)
[-1.99] **
-2.336 (5)
[-2.97] ***
-0.781 (3)
[-2.53] **
-1.361 (4)
[-4.14] ***
-0.666 (3)
[-2.32] **
-1.295 (4)
[-4.37] ***
-0.744
[-9.51] ***
21.687 (2)
[2.33] **
11.203 (8)
[1.71] *
10.165 (10)
[1.71] *
-3.165 (1)
[-3.99] ***
-3.284 (1)
[-4.20] ***
Turkey
-1.055
[-11.44] ***
9.429 (5)
[2.26] **
23.992 (6)
[5.31] ***
11.761 (7)
[2.52 ]**
9.284 (8)
[2.54] **
-0.284 (1)
[-2.24] **
-0.487 (2)
[-2.99] ***
-0.528 (3)
[-3.24] ***
-0.372 (4)
[-2.51] **
UK
0.824
[-7.43] ***
24.286 (1)
[2.04] **
41.078 (7)
[3.21] ***
US
-1.112
[-15.15] ***
-0.741 (2)
[-2.30] **
-20.285 (8)
[-1.75] *
-46.895 (12)
[-4.36] ***
-17.076 (16)
[-1.91] *
-13.840 (16)
[-1.94] *
-8.959 (2)
[-1.76] *
-15.113 (3)
[-3.36] ***
-9.097 (7)
[-1.69] *
-14.563 (8)
[-2.67] ***
-18.329 (11)
[-2.77] ***
-11.772 (16)
[2.60] ***
-9.062 (19)
[-2.54] **
-23.178 (1)
[-1.94] *
-25.077 (10)
[-2.66] ***
-18.433 (13)
[-2.93] ***
-10.504 (16)
[-1.77] *
-15.011 (1)
[-5.92] ***
-17.733 (2)
[-5.96] ***
-18.282 (3)
[-5.49] ***
-21.097 (4)
[-6.93] ***
-21.672 (5)
[-6.56] ***
-9.214 (6)
[-3.11] ***
-3.053 (19)
[-1.72] *
-10.842 (4)
[-1.93] *
-28.748 (7)
[-4.25] ***
-16.479 (9)
[-2.82] ***
-10.599 (15)
[-1.72] *
-15.321 (17)
[-2.17] **
-16.383 (2)
[-3.03] ***
-15.734 (18)
[-3.71] ***
0.466
0.401
(𝑝 = 0.54)
0.552
0.297
(𝑝 = 0.59)
0.597
1.973
(𝑝 = 0.18)
0.862
0.272
(𝑝 = 0.61)
0.382
1.557
(𝑝 = 0.23)
-0.676 (2)
0.719
0.514
[-1.97] **
(𝑝 = 0.48)
-0.921 (3)
[-2.20] **
Source Author’s calculation using the software package STATA 14
Notes: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. p is the probability. ( ) is the lag length.
[ ] is the t-ratio. a Reporting only those variables (namely 𝑦, 𝜋, 𝑟 and 𝑒) that are significant and that have the expected sign.
To obtain more robust results, this study identifies the stock
market catch-up effect based on 30 selected countries;
specifically, the study detects stock market return catching-up
effects between 29 countries’ stock market return and the US
stock market return. In this context, we include the economic
catastrophe of the period as the benchmark that garnered
international infamy by determining the sample periods from
1995 quarter one to 2018 quarter one; those benchmarks
Journal of critical reviews
include three global economic recession eras, namely, the year
1998, the years 2001–2003, and the years 2008–2009). Table 4
summarizes the catch-up estimation results for the stock
market return. By characterizing the six quantification
quarterly periods during and after the global economic
recessions, the average catch-up rates suggest that there is a
widening (diminishing) disparity between 29 countries’ stock
market returns and the US stock market return during (after)
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STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
the recession; in response to the recessions, the presence of
widening disparity may be due to the absence of a consistent
regime or strategy among multilateral institutions, national
policymakers, and the economics’ profession (Grabel, 2011).
Overall, the fact that the average catch-up rate of the full sample
period has been narrowing shows that convergence does hold
for 29 countries’ stock market return (Note that, although there
are some unfavorable results arising from the negative catch-
up rate (i.e., widening disparity), the presence of convergence
remains sensible because the negative rate is close to zero). In
addition, a similar procedure (i.e., the catch-up estimation) is
also applied to leading macroeconomic indicators (i.e., 𝑦𝑡 , 𝜋𝑡 , 𝑟𝑡 ,
and 𝑒𝑡 ). Table 5 summarizes the catch-up estimation results for
𝑦𝑡 , 𝜋𝑡 , 𝑟𝑡 , and 𝑒𝑡 . The average catch-up rates suggest that the
convergence of 29 countries’ leading macroeconomic indicators
is present.
Table 4 Average catch-up rates of the stock market return (the convergence of 29 countries towards the US).
Countries
Quarterly period from
1995
to 1998 a
1999
to 2001 to 2004
to 2008
to 2010
to
2018
2000
2003 a
2007
2009 a
2018
Argentina
-0.272
-0.895
0.239
-0.099
0.021
-0.676
-0.276
Australia
-0.024
-0.453
0.140
-0.130
0.069
-0.018
0.036
Brazil
2.325
4.791
3.003
-1.241
0.852
1.258
-1.006
Canada
0.000
-0.009
0.587
-0.416
-0.039
0.022
0.030
China
-0.008
-0.423
0.592
-0.515
0.195
-0.121
0.037
Colombia
-0.092
-1.179
0.714
-0.451
0.021
0.095
-0.010
Czech Republic
-0.134
-1.278
0.206
-0.163
-0.001
-0.183
0.013
Denmark
-0.039
-0.400
0.446
-0.130
0.041
-0.171
-0.017
Finland
-0.009
-0.409
-0.081
-0.107
0.040
-0.057
-0.022
Hong Kong
-0.347
-1.778
-0.361
-0.035
0.095
-0.680
0.128
Indonesia
-0.049
-4.437
0.793
-0.619
-0.019
-0.160
0.081
Ireland
-0.005
-0.157
0.157
-0.097
0.099
-0.306
0.023
Italy
-0.043
0.618
0.083
-0.187
0.045
-0.019
0.203
Japan
-0.005
-1.516
0.575
-0.325
0.061
-0.067
0.003
Malaysia
-0.069
-0.999
-0.171
-0.212
-0.077
0.087
-0.012
Mexico
-0.057
-2.540
1.445
-0.765
0.112
-0.262
0.076
New Zealand
-0.043
-0.492
0.318
-0.451
0.103
-0.136
0.005
Philippines
0.032
-4.157
1.522
-0.601
-0.003
-0.102
0.067
Poland
0.218
-2.950
0.426
-0.246
0.130
-0.141
0.010
Russia
0.076
-3.671
5.714
-2.519
0.231
0.088
-0.086
Singapore
-0.023
-0.713
-0.231
-0.144
0.077
-0.085
-0.035
South Africa
-0.031
-1.663
0.725
-0.098
-0.068
-0.099
0.020
South Korea
0.005
-3.783
0.271
-0.674
-0.013
-0.127
0.030
Spain
-0.040
-0.290
0.621
-0.487
0.102
-0.293
0.022
Sweden
-0.128
-1.257
1.168
-0.582
0.113
-0.106
0.425
Switzerland
0.052
0.139
0.097
-0.066
0.070
0.223
0.184
Thailand
-0.044
-3.648
-0.395
0.147
-0.143
-0.175
0.004
Turkey
-0.403
-13.428
4.864
-2.313
0.156
-0.988
0.220
UK
0.007
-0.222
0.158
-0.137
0.030
-0.090
0.004
Average
0.029
-1.628
0.815
-0.471
0.079
-0.113
0.005
Source Authors’ calculation
Notes: The sample period is quarterly from the first quarter of 1995 to the first quarter of 2018. a indicates global economic recession
eras (i.e., 1998, 2001–2003 and 2008–2009), as described by the International Monetary Fund (2009).
Table 5 Average catch-up rates of the output, the inflation rate, the interest rate and the exchange rate (the convergence of
29 countries towards the US).
Countries
Quarterly period from 1995 to 2018
𝑦𝑡
𝜋𝑡
𝑟𝑡
𝑒𝑡
Argentina
0.614
0.165
0.559
0.078
Australia
0.058
0.013
0.110
0.051
Brazil
0.530
0.139
1.029
0.056
Canada
0.233
-0.003
0.114
0.053
China
0.431
0.113
-0.123
0.047
Colombia
0.128
0.061
-1.048
0.054
Czech Republic
-0.423
0.055
0.188
0.043
Denmark
-0.345
0.014
0.175
0.056
Finland
0.019
0.001
0.159
0.059
Hong Kong
0.066
0.044
0.090
0.059
Indonesia
-0.185
0.031
0.130
-0.746
Ireland
-0.127
0.005
0.186
0.054
Italy
-0.328
0.019
0.191
0.055
Japan
-0.346
0.009
0.062
0.065
Malaysia
-0.159
0.018
-0.015
0.064
Mexico
0.105
0.024
0.153
-0.001
New Zealand
0.425
0.021
0.058
0.053
Philippines
0.295
-0.010
0.110
0.057
Poland
-0.423
0.176
0.254
0.049
Russia
0.365
0.907
6.473
0.040
Singapore
-0.357
0.011
0.032
0.056
South Africa
2.191
0.031
-0.104
0.061
Journal of critical reviews
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STOCK MARKET INTEGRATION IN THE PRESENCE OF LEADING MACROECONOMIC INFORMATION: EVIDENCE
FROM 30 SELECTED COUNTRIES
South Korea
1.885
-0.011
0.232
0.059
Spain
-0.285
0.027
0.200
0.054
Sweden
0.050
0.013
0.224
0.061
Switzerland
0.054
0.000
0.168
0.056
Thailand
0.307
0.016
0.052
0.057
Turkey
0.134
0.409
1.000
0.046
UK
0.063
0.004
0.156
0.058
Average
0.172
0.079
0.373
0.026
Source Authors’ calculation
Notes: The sample period is quarterly from the first quarter of 1995 to the first quarter of 2018.
The results yield some noteworthy findings. First, the results of
both the cross-section dependence test and the panel causality
test suggest the existence of interdependence among countries
in terms of stock market return, output, inflation rate, interest
rate, and exchange rate, and that leading macroeconomic
indicators (i.e., output, inflation rate, interest rate, and
exchange rate) can influence stock market return, respectively.
Second, the results of the heterogeneous panel cointegration
test corroborate the above finding that there exist a long-run
relationship between stock market return and its determinants,
namely, inflation rate, interest rate and exchange rate, except
for output. In line with this finding, the short-run dynamics of
the variables are affected by the deviance from the long-term
equilibrium. Note that, because of the presence of a positive
relationship between stock market return and output in the
short-run, the absence of a positive long-run relationship
between stock market return and output does not decisively
refute the explanatory power of the output variable. This
absence may be due either to long-run stock market returns
depending on dividend earnings and the growth of per share
dividends (Ritter, 2005) or to stock markets revealing
information rapidly through price changes (Stiglitz, 1993).
Overall, these results reveal that the leading macroeconomic
indicators are important economic forces in shaping the
integration of stock markets across national borders. Third, the
results of both the catching-up effect of stock market return and
the catching-up effect of leading macroeconomic indicators are
found to be important in explaining the convergence of
integrated countries.
The findings of the current study offer some policy implications.
The paper suggests that the leading macroeconomic indicators
(i.e., output, inflation rate, interest rate and exchange rate) can
serve as (i) an ongoing source of support for traders (e.g.,
investors, portfolio managers and investment agents) as an
attempt to optimize stock return from investments across
national borders and (ii) a buffer of the country’s regulatory
support system for policymakers by avoiding economic
catastrophes arising from excessive stock return co-movement
across national borders driven by macroeconomic uncertainty
(e.g., output uncertainty, inflation uncertainty, interest rate
uncertainty, and exchange rate uncertainty). Upon doing so,
leading macroeconomic indicators play an important role in
shaping the convergence of stock markets –– the stock market
integration –– that eventually helps both policymakers and
traders to achieve mutual benefits and to promote stable
economic conditions. Our findings also suggest that leading
macroeconomic indicators are the key elements of
policymakers in shaping not only stock market integration but
also economic integration.
CONCLUSIONS
The paper examines the integration of stock markets across
national borders with leading macroeconomic indicators based
on a sample of 30 countries (i.e., Argentina, Australia, Brazil,
Canada, China, Colombia, Czech Republic, Denmark, Finland,
Hong Kong, Indonesia, Ireland, Italy, Japan, Malaysia, Mexico,
New Zealand, Philippines, Poland, Russia, Singapore, South
Africa, South Korea, Spain, Sweden, Switzerland, Thailand,
Turkey, the UK, and the US). The results obtained using the
proposed methods, namely, the cross-section dependence
method, the heterogeneous panel cointegration method, and
the catch-up method, support the hypothesis that the leading
Journal of critical reviews
macroeconomic indicators should help foster the convergence
of stock markets –– the stock market integration –– and help
both policymakers and traders to achieve mutual benefits and
to promote stable economic conditions. Thus, the leading
macroeconomic indicators fulfill its role as (i) an ongoing
source of support for traders, as an attempt to optimize stock
return from investments across national borders and (ii) as a
buffer of the country’s regulatory support system for
policymakers by avoiding economic catastrophes arising from
excessive stock return co-movement across national borders
driven by macroeconomic uncertainty. Moreover, our results
confirm and extend previous findings by Kose et al. (2017) that
there are financial and economic linkages between the US and
the rest of the world.
This study has several limitations. First, the study sample
included only 30 countries and four leading macroeconomic
indicators, namely, output, inflation rate, interest rate, and
exchange rate. Several areas for future research would, thus,
include other potential macro-environmental variables (e.g.,
sociocultural, technological, political-legal, and international
variables) in the current practice of deriving these
specifications for stock market integration; however, it is
noteworthy that encompassing a number of different variables
would inevitably complicate a diagnosis of stock market
integration. Furthermore, a process similar to that executed in
this study can also be replicated in different countries and
regions of the world. Second, other uncertainty issues in the
design and measurement of the integration of stock markets
across national borders are left for future research.
Acknowledgments
The author would like to thank the anonymous reviewers for
their comments and suggestions. The author acknowledges
funding support from the “Fundamental Research Grants
Scheme (FRGS), Ministry of Education Malaysia” under Grant
2019-0148-106-02 (FRGS/1/2019/STG07/UPSI/01/1).
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