Title of the Paper:
An Empirical Investigation of Linkages between India and Major Asian Stock Markets
Author’s Name: Dr. Rajkumar Giridhari Singh
Affiliation: Assistant Professor, Department of Management, Mizoram University, Tanhril,
Aizawl-796004 (Mizoram)
Email id: rkgiridhari@gmail.com
Mobile number: 09862568266
An Empirical Investigation of Linkages between India and Major Asian Stock Markets
Abstract
The paper attempts to understand the inter-linkages and causal relationships between the stock
exchanges. This study covers Tokyo Stock Exchange (TSE), Hong Kong Stock Exchange (HSE),
Bombay Stock Exchange (BSE) and National Stock Exchanges (NSE). To establish the
relationship, the daily closing data of the stock indices such as the Nikkei in Japan, Hangseng in
Hong Kong with that of NSE Nifty and BSE Sensex in India during the period March 2007 to
August 2014 are used. The Granger-causality and Co-integration test were used to check the
causal relationship. The study found that there is uni-directional short-term causal influence from
Indian stock markets to the Japanese and Hong-Kong stock market while no long-term
relationships are found between the Indian and Japanese market as well as with the Hong Kong
market over the study period.
Keywords: Inter-linkages, Stock market, Granger’s causality test, Co-integration.
Introduction:
We are witnessing the widening and intensifying links between the countries. They are
becoming more closely linked with the growing volume of cross-border transactions in terms of
goods, services and capital flows. Over the years, the financial markets also have become
increasingly global. The integration of the world’s economies has accelerated with the gradual
lifting of restrictions on capital flows and relaxation of exchange controls in many countries. The
study of inter linkages among economies hold relevance as this global integration has brought
significant opportunities and challenges to the countries. The globalization of the world stock
markets is one of the most significant developments that have occurred during the last two
decades. The stock market has gained its importance because of its facility in raising capital and
its movements. The advancement in information technology, telecommunication and the
emergence of new international financial institutions offering financial services has expedited the
flows. Today, the investment opportunities are no longer restricted to domestic markets.
Investors can approach overseas market to seek profitable opportunities. The global markets
have become more accessible. The knowledge of international stock market is significant for
portfolio managers and investors. Due to this fact, the researchers world-wide have keen interest
in the performances of the stock markets and its inter-linkages.
Thus the present study is an analysis of the inter-linkages of the Indian stock markets
with the stock markets of Japan and the Hong Kong. The Japan and Hong Kong are two most
advance markets not only in Asia but also from the global context too. The paper attempts to
examine the inter-linkages and causal relationship between the Nikkei 225 index of Japan Stock
Exchange in Japan and the Hangseng index of the Hong Kong Stock Exchange in Hongkong
with that of NSE Nifty and BSE Sensex in India during the period of the period March 01, 2007
to August 31, 2014. Established in 1878, and based in Tokyo, Japan, the Tokyo stock exchange
lists 2,292 companies as of 2012. It is the largest stock exchange in Asia, and the third largest in
the world, having a market capitalization of $3.478 trillion. The Nikkei 225 is one of the main
indices of Tokyo Stock Exchange. With a market capitalization of $2.831 trillion, Hong Kong
Stock Exchange is the second largest stock exchange in Asia and the sixth largest in the world in
terms of market capitalization. It was first established as “Association of Brokers, Hong Kong”
in the year 1891, and was renamed in 1914. The Hong Kong Stock Exchange lists 1,470
companies from around the world.
According to the theories of finance, the investors can achieve a better risk-return tradeoff by having a well diversified portfolio. According to the Portfolio theory of Markowitz
(1952), the diversification of portfolio is beneficial when the correlation among the assets is
negative. An international investor who is willing to make an international portfolio investment
in different stock markets is interested to understand if diversification can give some gain or not.
If the stock markets move together then investing in different national stock market would not
generate any gain. Therefore, the analysis of the relationship between the stock markets will
facilitate global investors in reaching a better decision.
In the recent past, international portfolio diversification was recommended in the pretext
of low correlation and integration among different stock markets. Over the time the economies
are opening up, there is a growing international trade, investment flows, deregulation of financial
systems and growth in the cross border capital flows. This path of liberalization has led to
closely linked national economies. As the underlying economies become more closely
integrated, stocks markets may become more correlated. This would reduce the benefits of
international diversifications. The present study undertaken will benefit to have an understanding
the intensity of stock market integration. A study of stock market movements and integration is
significant as the knowledge of this area can equip investors and policy makers to take better
decisions.
Literature Review:
The interest in studying the inter-linkages between stock exchanges is pervasive in all
regions of the world. Various studies have undertaken in different parts of the world with regard
to the stock market linkages. However the literature on the origin of market portfolio traces back
to theoretical postulates of Markowitz (1952), Sharpe (1964), Lintner (1965), Ross (1976), Fama
and French (1996), etc. The literature review shows that there is conflicting evidence on the issue
of international stock market linkages.
In attempting to understand the international transmission mechanism of stock market
movements, Eun & Shim (1989) estimated a nine-market vector-autoregressive system using
daily rates of return on the stock market indices from the period January 1980 through December
1985. The daily return data from the nine markets viz. Australia, Canada, France, Germany,
Hong Kong, Japan, Switzerland, United Kingdom, and United States were used. The study found
that a substantial amount of interdependence exists among national stock markets. The U.S.
stock market is found to be, by far, the most influential market in the world. No national stock
market is nearly as influential as the U.S. in terms of its capability of accounting for the error
variances of other markets.
Taking the sample of the eight countries viz. Canada, Germany, France, Netherlands,
Switzerland, United Kingdom, Japan and United States, Cochran & Mansur (1991) uses Pairwise Granger tests to investigate the effects of uni-directional causality, bi-directional causality,
and contemporaneous adjustment in the determination of market rates of return. The first
differences of weekly returns are used in the empirical analysis and the causality tests are
performed on an annual basis over the 1980-89 time frame, as well as for the sub-periods 198089, 1980-85III, and 1985IV- 89 periods. The study found the existence of significant unidirectional and bi-directional effects which suggests that the international equity markets are not
completely integrated. They conclude that international diversification can result in a reduction
in portfolio risk, however, due to the apparent instability in the level of capital market
integration, the ability to diversify internationally may vary over time.
A paper by (Ahmad, Ashraf, & Ahmed, 2005) analyses the inter linkages and causal
between the Nasdaq composite index of US, the Nikkei of Japan with that of NSE Nifty and BSE
Sensex in India during the period January 1999 to August 2004 using daily closing data. The
study using the Co-integration test and Granger causality test found out that there was no longterm relation (no co-integration) of the Indian equity market with that of US and Japanese
however, the US and Japanese market had the short-term causal influence over Indian stock
market for the study period.
In an attempt to understand the volatility of market returns, Fayyad & Daly (2010) did a
comparative study of United Arab Emirates (UAE) equity market , Kuwait Equity market with
the equity markets of United States of America (USA), United Kingdom (UK). Taking the data
from the period between 5th October, 2005 through 5th October, 2009, MGARCH model was
used in the modeling. The study found the volatility for the emerging markets of Kuwait and
UAE are more volatile than the advanced markets of USA and UK over the study period. The
study also found that UAE market is relatively highly correlated with the advanced markets
return of UK and USA comparing to Kuwait market which is highly bidirectional correlated to
the regional markets in the Gulf area.
Singh & Singh (2010) analyses of the linkages of stock markets of U.S., U.K., Japan,
Hong Kong with Chinese and Indian markets by using the correlation test, Granger causality and
the co-integration test applying Error Correction Model. The study found both Chinese and
Indian markets are correlated with all four developed markets under study namely U.S., U.K.,
Japan and Hong Kong.
Subhani, Hasan, Mehar, & Osman (2011) studied the daily co-movement of the four
Indices comprising of KSE-100 from Karachi Stock Exchange (Pakistan), BSE Sensex (India),
DSE Composite Index (Bangladesh), and NSE Index (Nepal) was examined by using the
Johansen co-integration analysis for the period of May-1995 to May-2011. The study found that
there is linkage of stock prices between Karachi Stock Exchange and the stock prices of Dhaka
Stock Exchange, while KSE is not co-integrated with the neither India nor Nepal.
In a paper by Singh & Sharma (2012) studying the linkages between the Stock exchanges
of Brazil, Russia, India and China found that the Russian, Indian and Brazilian stock exchanges
affects each other and get affected by their own return but none of these affect Chinese stock
exchange. The Granger’s causality model, Vector Auto Regression (VAR) model and Variance
Decomposition Analysis were performed by using the data of 60 months from 1st April, 2005
through 31st March, 2010.
Tripathi & Sethi (2012) found positive and significant correlation of Indian market with
the Brazil, Hungary, Taiwan, Mexico, Poland and South Africa. The study uses the daily closing
index value of the leading indices of the above countries for the period from January 1, 1992 to
December 31, 2009. The study uses the co-integration framework to examine the long-run
relationships while the Granger causality tests were used to test the short term causality.
The stock markets integration has been analysed by researchers ( (Vanitha, Srinivasan, &
Karpagam, 2011); (Lamba, 2008); (Agmon, 1972); (Hilliard, 1968); (Tripathi, Seth, & Kumar,
2013); (Roca, Selvanathan, & William, 1998); (Daly, 2003); (Yang, Khan, & Pointer, 2003);
(Dhal, 2009), etc. The interlinkages has been checked with equity markets of both developed and
the emerging markets. The results have been found mixed evidence in the study above
mentioned
Objective:
The following are the objectives of the study undertaken:
(i)
To find whether Indian stock market is inter-linked with the leading Asian markets in
the long-run.
(ii)
To determine the short-term relationship between the Indian stock market and the
stock market of the two leading major Asian markets
Methodology:
Sample:
The present study is based on a time-series of daily data for the period March 01, 2007 to
August 31, 2014. The Table 1 shows the sampled stock exchanges and their respective details.
TABLE 1
Stock Exchange and Stock Indices
Sl. No
Country
Stock exchanges
Index
Symbol
1
India
Bombay Stock Exchange
Sensex
Sensex
2
India
National Stock Exchange
S&P CNX Nifty
Nifty
3
Japan
Japan Stock Exchange
Nikkei 225
Nikkei
4
Hong Kong
Hong Kong Stock Exchange
Hang Seng
Hangseng
Data:
The daily closing prices for Nifty and Sensex are collected from nseindia.com and
bseindia.com respectively. The data for Nikkei and Hangseng index are taken from
Investing.com. The daily returns in the sample stock markets are matched by the calendar date.
The holidays and days with no trading have been omitted from the paired series. Thus, if there is
a holiday in any of the country the data have been removed for both the countries – the Hong
Kong and India and similarly Japan and India. To make the paired series comparable and the
observation equal, the equity price changes has therefore been calculated from the last day when
both the markets are open.
The period is divided into two sets of years i.e. 2007-2010 and 2011-2014 in order the
capture the effect and movement of stock exchanges with each other during different sub
periods. The first sub-period 2007-2010 represents the global economic crisis period while the
period 2011-2014 represents the recovery of the global economic crisis.
Tools used: In order to achieve the objective of the paper, the following tests were undertaken:
1) Testing for stationarity of the data is done by using the Augmented Dickey-Fuller method
(ADF) and Philips-Perron (PP) tests.
2) For Causality Test, the Co-integration test and Granger test is used to identify whether
one series has a significant explanatory power for the other series or not.
3) The daily return of the index are calculated by taking the natural logarithm of the daily
closing price relatives as follows
r = ln(Pt/Pt-1) x 100
where, ln is the natural log, Pt is the closing price of today and Pt-1 is the closing price of the
previous trading day.
Hypotheses:
(a) H01: Nikkei does not Granger-cause Nifty or Sensex, and Hangseng does not Grangercause Nifty or Sensex.
(b) H02: There is no co-integration between Japanese and Indian equity markets or between
the Hong Kong and Indian equity markets.
Empirical Analysis:
Unit Root Test Results:
Table 2 and 3 summarises the results of the unit root tests for the indices series and the
return of the indices respectively. The ADF and PP tests for stationarity are applied in three
forms: without drift and time trend; with drift and no time trend and with drift and time trend.
All the three forms to test the series for stationarity have estimated and found that the results are
invariant to the model specification except minor differences in the ADF and PP values.
Therefore, only the results of ADF and PP tests based on drift and without time trend are
reported in the Table 2 and 3.
It can be concluded that the index series are non-stationary in the total time periods as
well as sub-periods 2007-2010 and sub-period 2011-2014. Whereas the unit root test results for
the return series show that the stock markets are stationary. The same results are obtained by
both the ADF and PP tests indicating the robustness of the findings.
TABLE 2
Unit Root Test Results for the Indices
ADF Test Statistics
PP Test Statistics
2007-2014 2007-2010 2011-2014 2007-2014 2007-2010 2011-2014
Nifty
-1.0883
-1.1865
0.2091
-0.7768
-1.1713
0.3003
Sensex
-0.9755
-1.1518
0.6122
-0.6600
-1.1276
0.4478
Nikkei
-1.8008
-1.5654
-0.3634
-1.7542
-1.6756
-0.3960
Hangseng
-2.2777
-1.5698
-1.8916
-2.2849
-1.6066
-1.8901
Note: MacKinnon critical values for the rejection of unit root test at 1% are -3.4340,-3.4377
and -3.4382 for the respective sample sizes
Variables
TABLE 3
Unit Root Test Results for the Returns
ADF Test Statistics
PP Test Statistics
2007-2014 2007-2010 2011-2014 2007-2014 2007-2010 2011-2014
RNifty
-8.7568
-10.5117
-26.6556
-39.7386
-28.8232
-26.6356
RSensex
-8.5946
-10.5185
-10.4074
-39.2903
-28.4828
-26.3200
RNikkei
-30.7096
-6.8488
-29.4918
-41.5737
-29.5630
-29.4914
RHangseng
-7.5511
-6.2869
-28.5677
-41.7216
-30.2906
-28.5677
Note: MacKinnon critical values for the rejection of unit root test at 1% are -3.4340,-3.4377
and -3.4382 for the respective sample sizes
ADF-Augmented Dickey Fuller Test statistics, PP-Philip Perron Test statistic
Null Hypothesis: the series in non-stationary
Variables
Co integration Test Results
To check the long-term relationship, the Johansen co-integration test between the indices
for the total time periods as well as the two sub-periods was conducted. Two variables will be
cointegrated if they have a long-term, or equilibrium relationship between them (Gujarati, Porter,
& Gunasekar, 2009). The Johansen method is applied in the present study. The method can be
illustrated by suing the following general autoregressive representation for the vector Y.
𝑌𝑡 = 𝐴1 𝑌𝑡−1 + … . . +𝐴2 𝑌𝑡−2 + . … + . . . . 𝐴𝑝 𝑋𝑡−𝑝 + 𝜀𝑡
… . . (1)
Where, Yt is a (n x 1) vector on non-stationary I(1) variables, p is the number of lags, ɛt
is an independently and identically distributed n-dimentional vector with zero mean and
variance matrix
The above equation can be reparametrized and turned into a vector correction model as:
𝑝−1
∆𝑌𝑡 = 𝜋𝑌𝑡−1 + ∑ 𝜋𝑖 ∆𝑌𝑡−𝑖 + 𝜀𝑡
… … … … … . (2)
𝑖=1
Where,
𝑝
𝜋 = −(𝐼 − ∑ 𝐴𝑖 )
… … ..
(3)
𝑖=1
And,
𝑝
𝜋𝑖 = − ∑ 𝐴𝑗
… … … . (4)
𝑗=𝑖+1
The π can be interpreted as a long-run coefficient matrix, since in equilibrium, all the
ΔYt-p will be zero, and setting the error terms, ɛt, to their expected value of zero will leave π ΔYt-p
= 0. The key feature in (2) is the rank of the matrix π, the rank of π is equal to the number of
independent cointegrating vectors. The Johansen test centres on the examination of the π matrix
via eigen values. If the rank π = 0, the matrix is null and (2) becomes the usual VAR model in
first difference. If the rank π = 1, there is a single cointegrating vector and the expression πY t-1 is
the error correction term. For other cases in which 1<rank(π)<n, there are multiple cointegrating
vectors.
We can obtain the estimates of π and its characteristics roots. To test for the number of
characteristics roots that are significantly different from unity can be conducted using the Trace
test and Maximum Eigenvalue statistics (Enders, 2010).
TABLE 4
Johansen Co-integration Test between India and Japan (Nifty -Nikkei)
Period
2007-2014
2007-2010
2011-2014
Hypothesized
No. of CE(s)
Trace
Statistics
Critical
Value
Prob
value
Maximum
Eigenvalue
Statistics
Critical
Value
Prob
value
None
At most 1
None
At most 1
None
At most 1
7.512064
0.435645
4.937078
1.467575
3.375622
0.067123
15.49471
3.841466
15.49471
3.841466
15.49471
3.841466
0.5189
0.5092
0.8155
0.2257
0.9473
0.7956
7.076419
0.435645
3.469504
1.467575
3.308499
0.067123
14.2646
3.841466
14.2646
3.841466
14.2646
3.841466
0.4801
0.5092
0.9106
0.2257
0.9241
0.7956
* significant at 5%
TABLE 5
Johansen Co-integration Test between India and Japan (Sensex -Nikkei)
Period
Hypothesized
No. of CE(s)
Trace
Statistics
Critical
Value
Prob
value
Maximum
Eigenvalue
Statistics
Critical
Value
Prob
value
None
At most 1
None
At most 1
None
At most 1
7.189243
0.393258
4.630512
1.326443
4.341733
0.186825
15.49471
3.841466
15.49471
3.841466
15.49471
3.841466
0.556
0.531
0.847
0.249
0.874
0.666
6.795985
0.393258
3.304069
1.326443
4.154908
0.186825
14.2646
3.84147
14.2646
3.84147
14.2646
3.84147
0.5136
0.5306
0.9245
0.2494
0.8427
0.6656
20072014
20072010
20112014
* significant at 5%
TABLE 6
Johansen Co-integration Test between India and Hong Kong (Nifty-Hangseng)
Period
Hypothesized
No. of CE(s)
None
At most 1
None
2007-2010
At most 1
None
2011-2014
At most 1
* significant at 5%
2007-2014
Trace
Statistics
Critical
Value
Prob
value
Maximum
Eigenvalue
Statistics
Critical
Value
Prob
value
6.669873
0.397283
5.983724
2.022611
8.572013
0.005283
15.49471
3.841466
15.49471
3.841466
15.49471
3.841466
0.616
0.529
0.698
0.155
0.406
0.941
6.27259
0.397283
3.961113
2.022611
8.56673
0.005283
14.2646
3.84147
14.2646
3.84147
14.2646
3.84147
0.579
0.529
0.863
0.155
0.324
0.941
TABLE 7
Johansen Co-integration Test between India and Hong Kong (Sensex-Hangseng)
Period
Hypothesized
No. of CE(s)
None
At most 1
None
2007-2010
At most 1
None
2011-2014
At most 1
* significant at 5%
2007-2014
Trace
Statistics
Critical
Value
Prob
value
Maximum
Eigenvalue
Statistics
Critical
Value
Prob
value
6.703192
0.265222
5.668937
1.731326
10.2732
0.000189
15.49471
3.841466
15.49471
3.841466
15.49471
3.841466
0.612
0.607
0.734
0.188
0.26
0.991
6.437969
0.265222
3.937611
1.731326
10.27301
0.000189
14.2646
3.84147
14.2646
3.84147
14.2646
3.84147
0.5577
0.6066
0.8659
0.1882
0.1946
0.9908
Co integration Test Results:
Relationship between Japanese Market and Indian market:
To check the long-term relationship between India and Japanese Equity markets the
Johansen co-integration test between Nifty and Nikkei was first tested as shown in the Table 4.
The test is applied on the non-stationary indices series. There is no evidence of co-integration at
5 percent levels as indicated by trace statistics test in all the three periods. Similar results are
obtained from the Maximum Eigenvalue test in all the three periods among the two markets. A
similar test was conducted between Sensex and Nikkei. The results remain the same (Table 5) as
indicated both by trace statistics as well as Maximum Eigenvalue test for all the three periods.
Relationship between Hong Kong Market and Indian market:
As shown in the Table 6, to check the long-term relationship between India and Hong
Kong Equity markets, the Johansen co-integration test between Nifty and Hangseng was first
tested. The test is applied on the non-stationary indices series. There is no evidence of cointegration at 5 percent levels as indicated by trace statistics test in all the three periods. Similar
results are obtained from the Maximum Eigenvalue test in all the three periods among the two
markets. A similar test was conducted between Sensex and Hangseng. The results remain the
same (Table 7) as indicated both by trace statistics as well as Maximum Eigenvalue test for all
the three periods. Therefore it can be concluded that there is no long-term relationship between
Indian and Japanese markets as well as with the Hong Kong Market over the study periods.
Granger Causality Test Result:
Granger causality test shows the short-term relationship of precedence among variables.
It is applied on the stationary series. Hence, it is applied on the daily return series. Thus, Granger
causality test is conducted to further analyse the significance and direction of causality between
Japanese and Indian markets and Hong Kong and Indian markets. According to Granger (1969),
this test will answer the question of whether X causes Y. Y is said to be Granger-caused by X if X
helps in the prediction of Y, or equivalently if the coefficient on the lagged X are statistically
significant. To show that X Granger cause Y, first step is to consider an autoregression for Y.
Next, lagged values of X are added as the extra independent variables. Granger Causality test
results are very sensitive to the number of lags used in the analysis. There are different criteria
for specifying the lag length. This study adopts Schwartz information criterion (SIC) in which
lag 2 is found to be the optimal lag for the total time periods; for sub-periods 2007-2010 and
2011-2014, the optimal lags are found to be lag 1.The equation for the pair-wise Granger
causality tests are as follow:
𝑛
𝑛
𝑋𝑡 = ∑ 𝛼𝑖 𝑌𝑡−𝑖 + ∑ 𝛽𝑗 𝑋𝑡−𝑗 + 𝜇1𝑡
𝑖=1
𝑗=1
𝑛
𝑛
𝑌𝑡 = ∑ 𝜆𝑖 𝑌𝑡−𝑖 + ∑ 𝛿𝑗 𝑋𝑡−𝑗 + 𝜇2𝑡
𝑖=1
… … … … … . (5)
… … … … . . . (6)
𝑗=1
where, Xt and Yt = daily stock market index return for country X and Y respectively; μ1t and μ2t
are uncorrelated error term at time t with zero means and finite variance, i and j are number of
lags and n is the suitable maximum number of lagged observation included in the model which
is a positive integer.
The F test is used to test the hypotheses of the Granger Causality as follow:
H0a: βj = 0; the null hypothesis that Yt does not Granger-cause Xt is rejected if βj’s, j > 0 in (5) are
jointly different from zero using the F test. Similarly, H0b: δj = 0; the null hypothesis that Xt does
not Granger-cause Yt is rejected if δj’s, j > 0 in (6) are jointly significantly different from zero
using the F test.
As such, the null hypothesis is rejected if the computed F-value exceeds the critical F value at the
chosen level of significance (0.05). This implies that X does Granger cause Y. The test is
performed in pair-form between Japan and India as well as between Hong Kong and India.
TABLE 8
Pair-wise Granger Causality Tests Total period (2007-2014)
Null Hypothesis:
RSENSEX does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RSENSEX
RNIKKEI does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RNIKKEI
RHANGSENG does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RHANGSENG
RNIKKEI does not Granger Cause RSENSEX
RSENSEX does not Granger Cause RNIKKEI
RHANGSENG does not Granger Cause RSENSEX
RSENSEX does not Granger Cause RHANGSENG
RHANGSENG does not Granger Cause RNIKKEI
RNIKKEI does not Granger Cause RHANGSENG
Observation
1674
1674
1674
1674
1674
1674
F-Statistic
52.1791
2.79517
0.34619
27.8623
0.02573
18.4098
0.40572
31.1843
0.07695
24.0267
16.3598
0.30818
Prob.
1.00E-22*
0.0614
0.7074
1.00E-12*
0.9746
1.00E-08*
0.6666
5.00E-14*
0.9259
5.00E-11*
9.00E-08*
0.7348
Granger causality in the total time period (2007-2014):
The results of the granger causality test of the total time periods are summarized in the
Table 8 and it indicates whether there exists significant Granger Causality and if it exists, then in
which direction such causality exists among the various stock markets. The Table elucidates that
significant unidirectional causality are found between six pairs. The sensex has influenced all the
stock market indices viz. the Nifty, Nikkei and Hangseng. The Nifty has also influenced both the
Japanese and Hong Kong markets. However Nifty does not influenced the Sensex. The Table
also depicts that Hangseng influenced the Nikkei. Hence in the total time periods under study, it
is found that there is a significant unidirectional influenced from the Indian Stock Markets to the
two major major Asian markets.
TABLE 9
Pair-wise Granger Causality Tests (2007-2010)
Null Hypothesis:
RSENSEX does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RSENSEX
RNIKKEI does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RNIKKEI
RHANGSENG does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RHANGSENG
Observation
865
865
865
F-Statistic
6.72026
6.09985
0.13985
37.5966
0.01616
18.961
Prob.
0.0097*
0.0137*
0.7085
1.00E-09*
0.8989
1.00E-05*
RNIKKEI does not Granger Cause RSENSEX
RSENSEX does not Granger Cause RNIKKEI
RHANGSENG does not Granger Cause RSENSEX
RSENSEX does not Granger Cause RHANGSENG
RHANGSENG does not Granger Cause RNIKKEI
RNIKKEI does not Granger Cause RHANGSENG
865
865
865
0.02302
41.6837
0.03132
20.224
30.8786
0.01433
0.8794
2.00E-10*
0.8596
8.00E-06*
4.00E-08*
0.9048
Granger causality in the sub-period (2007-2010):
The results of the granger causality test of the sub-period (2007-2010) are summarized in
the Table 9. The Table elucidates that null hypothesis of no granger causality is rejected in case
of Nifty and Sensex. It means that both ways causality exists in these two stock markets in the
sub-period. As shown in the Table, the result reveals that there is a significant unidirectional
influenced from Indian stock market (both Sensex as well as Nifty) to the Japanese and Hong
Kong Markets. In this sub-period, Hangseng influenced the Nikkei.
TABLE 10
Pair-wise Granger Causality Tests (2011-2014)
Null Hypothesis:
RSENSEX does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RSENSEX
RNIKKEI does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RNIKKEI
RHANGSENG does not Granger Cause RNIFTY
RNIFTY does not Granger Cause RHANGSENG
RNIKKEI does not Granger Cause RSENSEX
RSENSEX does not Granger Cause RNIKKEI
RHANGSENG does not Granger Cause RSENSEX
RSENSEX does not Granger Cause RHANGSENG
RHANGSENG does not Granger Cause RNIKKEI
RNIKKEI does not Granger Cause RHANGSENG
Observation
810
810
810
810
810
810
F-Statistic
261.207
2.83477
0.00123
12.2582
0.06158
19.4152
0.2585
14.6832
0.3862
41.507
0.69436
1.70709
Prob.
4.00E-51*
0.0926
0.972
0.0005*
0.8041
1.00E-05*
0.6113
0.0001*
0.5345
2.00E-10*
0.4049
0.1917
Granger causality in the sub-period (2011-2014):
The results of the Granger causality test of the sub-period (2011-2014) are summarized in
the Table 10. As shown in the Table, the result reveals that there is a significant unidirectional
influenced from Indian stock market (both Sensex as well as Nifty) to the Japanese and Hong
Kong Markets. The sensex has influenced all the stock market indices viz. the Nifty, Nikkei and
Hangseng. The Nifty has also influenced both the Japanese and Hong Kong markets. However
Nifty does not influenced the Sensex. Hence in the sub-period (2011-2014), it is found that there
is a significant unidirectional influenced from the Indian Stock Markets to the two major Asian
markets.
Conclusion:
This paper examined the long-term and short-term inter-linkages of the Indian stock
market with the two major Asian markets viz. Nikkei of Tokyo Stock Exchange in Japan and
Hangseng of Hong Kong Stock Exchange in Hong Kong over the period of March 2007 through
August 2014 using daily data. The time period was divided into two sub periods 2007-2010 and
2011-2014 so as to check the changes in the inter-linkages if any, over the period of study. The
study found that there is no long-term relation (no co-integration) of the Indian equity market
with that of the Japanese and Hong Kong equity markets. Further, Sensex and Nifty have a shortterm causal influence over the Nikkei and Hangseng for the total time period 2007-2014. Taking
the sub-periods also the causal relationship remains unidirectional from Indian equity market to
the Japanese and Hong Kong equity market.
The study therefore concludes that both short-term and long run inter-linkages of Indian
Stock market with the advanced Asian stock market under the study period fail to show any
increase over the period despite the pace of globalization and various efforts undertaken. The one
plausible reason behind such results could be the reluctance of the investors due to the recent
global financial crisis that have adversely affected world-wide. The study has important
implications for the global and institutional investors who are now looking to invest beyond their
domestic markets. Therefore, portfolio diversification among these markets may reap benefits for
the global and institutional investors.
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