Interdependence of the Stock Markets, Before and During the...

Interdependence of the Stock Markets, Before and

During the Economic Crisis :The Case of East and South Asia

Mallika Appuhamilage K. SRIYALATHA＊

and Hiroshi TORII

Abstract

This study examines the stock market interdependence among the South and East Asian coun-

tries, namely Japan, Korea, India, Malaysia, Pakistan, the Philippines, Singapore, Sri Lanka and

Taiwan, before and during the current economic crisis. The data employed in the study is composed

of daily closing stock price indices over the period from January 3, 2000 to July 6, 2012. The

research methodology employed includes testing for stationarity both with the Dickey-Fuller and the

Phillips-Perron tests, the use of a VAR model and VECM for the implementation of the Granger

Causality test, and cointegration tests according to Johansen-Juselious. The results show that both

long-run cointegration relationships and short-run causal relations among these markets were streng-

thened after the economic crisis.

Key words : Market Interdependence, Cointegration, Granger Causality, Economic Crisis

1 Introduction

The global financial crisis was started from 2006 to 2007. Many Economists considered this as

the worst financial crisis in the world since the great depression in 1930s. This crisis started as a

subprime mortgage crisis primarily concentrated in the United States but quickly spread into a

global financial crisis where financial institutions teeter on the edge of bankruptcy in many

countries in addition to the United States. When US mortgage market was faced with the loss

then these losses affected the international financial system quickly. The confidence in many

financial institutions was strongly dropped and share prices decreased drastically at the end of

2007 and early 2008.

This financial crisis had serious influence on the financial institution, and share markets started

to decline shapely. In September 2008, the crisis deepened, as stock markets crashed and entered

a period of high volatility in the world wide. Major Asian stock exchanges of Bombay, Hong Kong,

Singapore and Tokyo were affected from this financial crisis. For example, the Nikkei255 index

139名城論叢 2013 年７月

＊Senior Lecturer, Department of Business Economics, University of Sri Jayewardenepura, Sri Lanka.

dropped from 18, 269 yen at its recent peak in July of 2007 to 7, 059 yen at the bottom in March of

2009 in Japan. On 26 December 2007 the Karachi-100 index in Pakistan closed at 14814 points

whereas it closed at 4929 points on 29 January 2009. Furthermore, the financial crisis spilt over to

Sri Lankan market. The All Share Index (ASI) of Sri Lanka declined by 16% during the month of

October 2008. However, at the beginning of the year 2009, Asian economy started to revive by the

support of International Monetary Fund (IMF) and stock markets seemed to regain their confi-

dence in the second half of 2009. The other reason to rapid recovery is that the effects of the

present sub-prime financial crisis on Asia are different from the previous case because Asian

countries went through its structural changes during the recovery from the Asian financial crisis

in 1997. This process might have also changed the transmission structure between Asian econo-

mies. Further, preliminary evidence shows that the recovery of Asian economies from this finan-

cial crisis is faster than that of the rest of the world and than their own recovery from Asian

financial crisis.

Although there has been large number of studies emphasizing on market integration and

interdependencies, very few of the existing studies have focused on the linkage between South

Asian stock markets. In the emerging markets, Sri Lanka has become one of the fastest growing

economies after its civil conflict against the Liberation Tamil Tigers (LTT) ended in 2009. In

2010, Sri Lanka’s Gross Domestic Product (GDP) grew by 8% while 3.5% in 2009, and has main-

tained 8% for the second consecutive year 2011. The land prices have increased substantially.

Further, enhancement of peaceful environment, the favorable political atmosphere, and improve-

ment in infrastructure projects like ports, highways, and airports are all leading to a gradual

return of foreign investment. Continued benefits and opportunities from the end of the long-

running civil conflict in 2009, such as improved business, tourist confidence and more land avail-

able to agriculture as well as the global return to growth, under-pinned the strong performance in

the economy. The overall optimism was reflected in the stock market’s doubling. According to

the Colombo Stock Exchange (CSE) Annual Report in 2010, the CSE performed as the second

best stock exchange in the world.

Paying attention to these factors, the objective of the present study will examine the interrela-

tions among Asian stock exchanges (developed and emerging) during the period of 2000 to 2012.

Following this introductory section, the recent empirical studies on the stock market inter-

dependence are surveyed in Section 2. In Section 3 the data and methodology applied for the

study are explained. In Section 4 the empirical results on the long-run and short-run structure of

interdependence are presented. Section 5 closes with brief concluding remarks.

2 Literature Review

Interdependence among the stock exchanges has been widely investigated. The early studies

have pointed out that the degree of interdependence of share price movements among markets is

第 14 巻第１号140

insignificant, and the primary determinants are the domestic factors rather than international. In

recent years, the capital inflow and outflow are almost free among the countries. At the same

time, transmission of information has become faster than before due to the technological advance-

ment and internet. As a result, price movements exhibit a substantial degree of interdependence

among stock markets.

Daly (2003) examined interdependence of the stock markets of Indonesia, Malaysia, the Philip-

pines, Singapore, Thailand, and the developed stock markets of Australia, Germany, and the US for

the period from 1990 to 2001. He used correlation and cointegration analysis to identify the

relationship between the markets, both before and after the 1997 Asian financial crisis. The

results indicate the increase of the interdependency across the Southeast Asian stock markets in

the aftermath of the crisis. Although he found integration between stock markets, the overall

results point out that the increased integration during the post crisis period cannot be seen.

Majid et al. (2008) examined long-run relationships for Association of South-East Asian Nations

(ASEAN) with the US. Awokuse et al. (2009) showed that the number of cointegrating vectors

increased in the post-crisis period among eleven Asian economies. Mukherjee and Bose (2008)

applied Johansen’s approach to seven Asian economies (India, Japan, Hong Kong, Korea, Malaysia,

Singapore and Taiwan) and the US for the post-crisis period between 1999 and 2005. They found

more than one cointegration vector among these countries using the daily data smoothed by a

moving average.

Mukherjee and Bose (2008) examined if the Indian stock market moved with other markets in

Asia. They employed techniques of cointegration, vector autoregression (VAR), vector error-

correction models (VECM), and Granger causality. The samples consisted of seven Asian econo-

mies (India, Japan, Hong Kong, Korea, Malaysia, Singapore and Taiwan) and the US for the post-

crisis period between 1999 and 2005. They indicated that there existed a definite information

leadership from the US market to all Asian markets, while Japan played an important role in the

integration of Asian markets.

Eun and Shim (1989) indicated the presence of interdependence among the stock exchanges in

the US, UK, Canada, Germany, France, Australia, Japan, Switzerland and Hong Kong. They

employed VAR to the daily closing price data for the period 1980-1985. They pointed out that

innovations in the US market were rapidly transmitted to the other markets. However, they did

not find that the foreign market gave the price movements in the US market.

Ibrahim (2005) examined the international linkage of Indonesian stock market in the pre-crisis

and post-crisis period. He employed time series techniques to test cointegration, and found an

evidence for no cointegration among the Indonesian market and the other Asian markets. Furth-

er, he pointed out that there is no cointegration relation with the US and Japan. Meanwhile he

found that there existed short-run relationship among Asian markets. The results further re-

vealed that the Indonesian market became more sensitive to the US and Japan during the post-

crisis period.

Interdependence of the Stock Markets, Before and During the Economic Crisis（SRIYALATHA・TORII） 141

Bracker et al. (1999) examined the co-movements between nine stock markets over 22 years,

and showed that bilateral import dependence, market size, and geographic distance were the

important factors. Chung and Ng (1991) found that information of the US market affected the

return of Tokyo stock exchange significantly on the next day, but they did not find influence from

Tokyo to the US market.

Kwan et al. (1995) investigated long-run relationship using cointegration analysis between

Australia, Hong Kong, Japan, Singapore, Korea, Taiwan, UK, US, and Germany for the period from

1982-1991. They used monthly data and found that these markets were not weak form efficient.

In a recent study, Majid et al. (2008) studied market integration among ASEAN from January

1, 1988 to December 31, 2006. The samples included emerging markets of Malaysia, Thailand,

Indonesia, the Philippines and Singapore and developed markets of the US and Japan. The results

indicated that the ASEAN stock markets were strengthening their integration either among

themselves or with the developed nations during the post financial crisis in 1997. Further, they

found that Indonesia was independent from the developed markets and Malaysia is dependent

more on Japan than the US. The Philippines is more Granger caused by the US than Japan, and

Japan and the US exhibited bidirectional causal relationship with Singapore.

Malliaris and Urrutia (1992) focused on interrelationships between stock markets under condi-

tion of the October financial crisis. They employed Granger Causality test to examine the direc-

tion of causality. They used daily data for the stock markets of the US, UK, Australia, Hong-Kong,

Japan and Singapore for the period May 1987 to March 1988. They did not find interdependence

before or after the crisis. Specially, they concluded that the US was no more the world dominant

market.

Cheung and Mak (1992) used weekly data of national stock market indices to examine a

financial integration for the period January 1978 to June 1988. They concluded that the US market

appeared to exert dominant influence to the most of the Asian pacific stock markets. Further they

showed that Japanese market had a less important influence on the Asian - Pacific emerging

markets. Roca (1999) investigated short and long-term price linkages between Asian equity

markets over the period December 1974 to December 1995. The results did not find any indica-

tion of cointegration between the stock markets.

Kanas (1998) examined relationship between the US and major six stock exchanges in Europe

for the period January 1983 to 1996. The study used monthly time series data and found that

there was no cointegration among the indices. Glezakos et al. (2007) investigated the interdepend-

ence between major world financial markets from 2000-2006. In the analysis, they paid their

special attention to the Athens stock exchange. Their sample included ten countries ; the

strongest financial market US, the leader in the Asian region Japan, the strongest markets in the

Europe (UK, Germany, and France), Italy, Spain, Holland, Belgium, and Greece. They employed

VAR methodology to test short and long-run relationship between stock exchanges. The results

showed that the US market was the dominant market in the world, while the influence of Germany

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and UK were also substantial on all other markets. Furthermore, they confirmed that Greek

capital market was Granger caused by the markets of Germany, Belgium, US and Italy.

Yusof and Majid (2006) investigated the long-run co-movements between Malaysian and the

two largest stock markets (US and Japan) . They employed both bivariate and multivariate

cointegration. The results indicated that the Japanese stock market significantly moved the

Malaysian stock market compared to the US stock market for the post crisis period.

Worthington et al. (2003) studied price linkages between nine Asian stock markets for the

period 1988-2000. Total sample period was divided into three sub periods based on Thai currency

crisis. They found evidence of price linkages between the equity markets before and after the

crisis. Further, they concluded that the most influential market in the pre crisis period became

less influential in the post crisis period.

Karim and Gee (2006) examined the relationship between Malaysia and its major trading

partners before and after the Asian financial crisis. The sample included the US, Japan, China,

Indonesia, the Philippines, Hong Kong and Thailand. They used cointegration and causality

testing to identify the relationship among the stock markets. The results showed that the exist-

ence of a long-run relationship between stock markets of Malaysia and the Philippines as well as

between Malaysia and the US before the financial crisis. Further they find that the short-run

causal relationship between Malaysian and the other contries started to weaken after the financial

crisis.

Gklezakou and Mylonakis (2009) studied the interdependence between the developing stock

markets of the South Eastern Europe, for the period 2000-2009 before and during the current

economic crisis. They employed Granger causality test to analyze the daily closing price data for

the seven countries. Their finding indicated that the interdependence of the stock exchanges

were strong during the current economic crisis. Further, they found that the developed stock

markets influenced the developing markets at the greater extent. Furthermore, they identified

that the Athens stock exchange plays an important role in the sample, since it affects most of the

emerging stock markets. Gklezakou and Mylonakis (2010) examined the interdependence among

ten markets using daily closing price data from 2000 to 2009. The sample included the US,

Belgium, France, Germany, Greece, Italy, the Netherlands, Spain, the UK and Japan. The empirical

findings indicated that the recent economic recession led to enhance their correlation, and tight-

ened their existing relations. Moreover, they found that while the direction of influence seemed to

be increased during the crisis, the leading role of the US and Germany was confirmed.

Lu et al. (2011) examined the interdependence of stock markets between India and its neigh-

boring countries (Sri Lanka and Nepal) from 2000 to 2008. They found that there was a causality

relation from India to Sri Lanka under the increasing of liquidity in the Colombo stock market in

recent years. Sriyalatha et al. (2012) examined the interdependence among the stock markets of

the US, Germany, the UK, Japan, Singapore and Sri Lanka. The study covered daily stock index

data for the period of 1990-2010. The results implied that under the context of globalization, the


stock markets had enhanced their interrelations after the recent economic crises. Especially an

emerging market of Sri Lanka was affected by almost all developed markets during the post

economic recession.

Elyasiani et al. (1998) examined the interdependence and dynamic relation between the

emerging markets of Sri Lanka and its major trading partners. They employed VAR technique

for daily data from 1 January 1989 to 10 June 1994. The sample consisted of the following

countries ; the US, Japan, India, Hong Kong, Korea, Taiwan, Singapore and Sri Lanka. They did

not detect any causal relationship between Sri Lanka and its trading partners and also indicated

that dynamic responses to external shocks were very low. Therefore, they concluded that there

was no significant interdependence between Sri Lanka and other equity markets. Further, they

pointed out that possible reasons for this weak relationship were small market capitalization, lack

of liquidity, high concentration in blue chip companies, and investment barriers on Sri Lankan

investors.

Despite the differences in econometric approaches, the majority of the studies indicates that

the degree of market integration among Asian economies increased either during or after the

Asian crisis period.

Our study differs from the existing studies on interdependence relation between Sri Lanka and

other counties in a few ways. We will carry out a comprehensive study with the long sample

period with the most recent data. The other aspect is that in recent years the liquidity in the Sri

Lankan market is increased substantially. Further, having overcome some of the bottlenecks for

investment in Sri Lanka, that is, political instability, monetary and fiscal disciplines last decades,

foreigners have revert their attention to Sri Lankan equity market. These benefits are experienc-

ing after the end of the civil conflict. The capital market started to boom and market capitalization

also has increased after the civil conflict. The prevailing literatures related to the financial market

interdependence indicate that almost developed markets are closely related and the emerging

markets are less integrated. This implies that the developed markets have fewer opportunities for

portfolio diversification. In this context also, it is important to identify the interdependence of Sri

Lanka with the other developed and emerging markets in the Asian region.

3 Data and Methodology

3.1 Data

The data employed in the study is composed of daily closing stock price indices in Asia over the

period from January 3, 2000 to July 6, 2012 for nine countries ; namely Japan, Korea, India,

Malaysia, Pakistan, the Philippines, Singapore, Sri Lanka and Taiwan. The sample consists of 3265

observations per country. All stock price indices are expressed in local currencies. Table 1 shows

the general stock price indices of the countries which make up the sample of this study.

The sample is divided into two sub-groups starting from 3/01/2000 to 20/7/2007 (Period A)

第 14 巻第１号144

and from 23/07/2007 to 6/07/2012 (Period B) which includes the current economic recession.

The study addresses the impact of this crisis in order to examine the stock markets’ interdepend-

ence under different market conditions.

3.2 Methodology

The daily returns of the indices are computed as follows.

R t=LogP t,LogP t-1

where R t is the daily return at time t. P t-1 and P t are daily closing prices of the indices at two

successive days, t,1 and t, respectively. According to the framework of the present study, the

following methodologies are employed to examine the interdependence between stock markets of

the Asian region.

3.2.1 Correlation between the Stock Indices

Returns of the daily indices are used to calculate correlation coefficients among the stock

markets. The correlation matrix shows that correlation coefficients for pairs of stock price indices.

It is estimated for two sub sample periods.

3.2.2 Testing for Stationary

We apply the Augmented Dickey-Fuller (ADF) Test and the Phillips-Perron (PP) Test to

check the stationarity of the data. It is noted that the data is considered to be stationary when the

mean and the variance do not depend on time. The ADF unit root test is widely used to test the

stationarity of the time series data. It shows whether an individual series is stationary by running

OLS regression. According to the ADF test, the null hypothesis is that the series has a unit root,

and if the ADF t-statistic is greater than the reported critical values, we can reject the null

hypothesis of non-stationary.


Table 1 Stock Exchange and Stock Market Indices

Country Stock Index Symbol

Japan Nikkei Index Nikkei-225

Korea Korea Composite Stock Price Index KOSPI

India Bombay Sensex Index SENSEX

Malaysia Kuala Lumpur Stock Exchange Composite Index KLSESI

Pakistan Karachi 100 Index Karachi-100

The Philippines PSE Composite Index PSECI

Singapore Straits Times Index Straits-Time

Sri Lanka CSE All Share Index CSE ASI

Taiwan Taiwan Weighted Index Taiwan WI

3.2.3 Examination of Cointegration and Causality

The correlation coefficient is a preliminary indication of the relationship between each pair of

share prices before and after the current economic crisis. It does not basically indicate the long-

run or short-run relationship in any meaningful sense.

Cointegration indicates the long-term common stochastic trend between non-stationary time

series data. If non-stationary series X and Y are both integrated of same order and there is a linear

combination of them that is stationary, they are called cointegrated series. However, cointegration

does not involve high correlation ; two series can be cointegrated but they may have very low

correlations. Cointegration tests allow us to determine whether stock indices of different national

markets move together over the long run, while providing for the possibility of short-run devia-

tions.

Meanwhile, systematic investigation of causal relationship is possible with an analytical

framework developed by Granger (1969) and Sims (1972). More specifically, it is pointed out that

X Granger-causes Y, if X gives statistically strong information in forecasting the future values of Y.

The existence of causality implies the direction of effect from one country to another. We use the

bivariate Granger causality test to examine the pair wise causality. The lag length of VAR model

or VECM is determined based on Alike Information Criteria (AIC). F-statistic is used to deter-

mine the significance of the test.

4 Preliminary Analysis and Empirical Results

4.1 Preliminary Analysis

During the sample period under investigation, seven out of nine stock markets showed positive

returns but in Japan and Taiwan the average returns of the indices indicate negative value as

shown in Table 2. The highest return came from the Sri Lankan and Pakistan markets and the

lowest from Japan. Korea reported the highest volatility and Malaysia the lowest, while the

Philippine Stock Exchange indicated a moderate level of return and risk.

第 14 巻第１号146

Table 2 Descriptive Statistic of Returns for the Stock Market Indices

Japan Korea India Malaysia Pakistan Philippines Singapore Sri Lanka Taiwan

Mean −0.000227 0.000158 0.000362 0.000204 0.0007 0.000281 4.37E-05 0.000662 −4.21E-05

Median 0.000000 0.000175 0.00011 0.000000 0.000247 0.000000 0.000000 0.000000 0.000000

Maximum 0.132346 0.112844 0.159901 0.198605 0.085071 0.161776 0.075305 0.305353 0.065246

Minimum −0.12111 −0.128047 −0.118092 −0.192464 −0.077414 −0.130887 −0.092155 −0.29677 −0.09936

Std. Dev. 0.015358 0.017205 0.016357 0.011279 0.014551 0.013596 0.012569 0.014613 0.01528

Skewness −0.410286 −0.563466 −0.179446 −0.238534 −0.224305 0.466999 −0.414551 0.311515 −0.21263

Kurtosis 10.23648 8.210847 9.619313 94.39905 6.160272 19.26832 8.926002 127.9178 5.755931

Jarque-Bera 7213.432 3863.147 5976.399 1136146 1385.645 36112.18 4869.468 2120958 1056.889

p-value 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00000

The returns of the most markets are skewed to the left (with the exceptions of the Philippines

and Sri Lanka). The negative skewness shows that large negative returns tend to occur more

often than positive ones. The coefficients of kurtosis are almost all larger than 3 indicating that the

tails of the distribution are all fatter than those of the normal distribution. The Jarque-Bera

statistic measures normality assumption and the results indicate that the sample stock returns are

not normally distributed.

Table 3 and 4 show the correlation coefficients of daily returns among the nine indices. The

values in Table 3 indicate the correlation coefficients for the pre current economic crisis. In

general, the markets under examination show a relatively low interdependence for the pre crisis

(Period A-3/1/2000-20/7/2007). More specially, during the current economic crisis the inter-

dependence between stock exchanges became stronger than before the crisis. Further, it is

evident from these Tables that the correlation coefficients between Sri Lanka and Pakistan are

immaterial in magnitude and implies that relatively low level of interdependence. Overall, the

information and innovations of the other markets do not seem to have an impact on Sri Lanka and


Table 3 Correlation Coefficient of Returns of the Stock Indices in the Period A


Japan 1 0.5107 0.2735 0.2716 0.0408 0.2312 0.4623 0.0427 0.3241

Korea 1 0.3240 0.2584 0.0645 0.2230 0.5119 0.0249 0.4384

India 1 0.1749 0.0970 0.1700 0.3572 0.0177 0.2476

Malaysia 1 0.0799 0.2262 0.3821 0.0148 0.2223

Pakistan 1 0.0924 0.0856 0.0269 0.0831

Philippines 1 0.2409 0.0170 0.1511

Singapore 1 0.0686 0.3493

Sri Lanka 1 0.0529

Taiwan 1

Table 4 Correlation Coefficient of Returns of the Stock Indices in the Period B


Japan 1 0.6758 0.3657 0.3145 0.1115 0.4780 0.5576 0.0530 0.5798

Korea 1 0.4537 0.3319 0.1308 0.4305 0.6353 0.0686 0.6974

India 1 0.2344 0.1087 0.2769 0.6023 0.0431 0.3914

Malaysia 1 0.0771 0.3192 0.3354 0.0516 0.3165

Pakistan 1 0.0825 0.1002 −0.0071 0.1254

Philippines 1 0.3665 0.0643 0.4690

Singapore 1 0.0715 0.5820

Sri Lanka 1 0.0403

Taiwan 1

Pakistan before as well as after the crisis.

In summary, it can be stated that relations between the nine share markets have strengthened

during the occurrence of the current economic crisis, but this conclusion remains uncertain due to

the limitations of correlation analysis. A deeper explanation needs the application of more

appropriate techniques, such as cointegration and causality testing.

4.2 Empirical Results

The ADF and PP tests are applied to the data to identify stationary properties of the indices.

To test for this property, both tests are conducted on the level and first-differenced series of stock

index for the whole period as well as the sub periods. In each stationary process, the constant or

the trend and the constant are included. The results reported here are only for the model with the

constant term. The results of the other models are also consistent with these results. The lag

order for the ADF test was selected by SIC. The test results on the levels and first-differences are

summarized in Tables 5 and 6 (See also Figure 1 and 2). The third column (A) of both tables

shows the t-statistic obtained in the period 3/1/2000 to 20/7/2007 (prior to the economic crisis).

The forth column (B) is for the period of 23/7/2007 to 6/7/2012. The final column (C) is for the

entire sample period (3/1/2000 to 6/7/2012). In all instances, the null hypothesis of nonstationar-

ity is tested under the ADF and PP test. Both tests indicated that for each series in level the null

hypothesis of unit root cannot be rejected at 1% level (Critical value at 1%＝3.43). The tests for

first-differenced series rejected the null hypothesis of unit root, that is, they are stationary for all

periods. Consequently, all nine series are integrated I (1). All series are strongly mean reverting

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Table 5 Results of the ADF Unit Root Tests (Level

Series)

tau-statistic

Country Index A B C

Sri Lanka CSE ASI −0.1166 −0.7964 −0.1714

Pakistan Karachi-100 1.0044 −1.1136 −0.6436

Malaysia KLSESI 1.0693 −0.6472 −0.0738

Korea KOSPI 1.9084 −1.7697 −0.7557

Japan Nikkei-225 −1.3862 −3.0281 −2.2424

Philippines PSECI 1.8042 0.1090 0.8477

India SENSEX 2.1156 −1.6068 −0.7171

Singapore Straits-Time 1.3607 −1.8867 −1.1345

Taiwan Taiwan WI −0.9781 −1.9805 −2.0952

Notes : Period A 3/1/2000-20/7/2007, Period B 23/7/2007-

6/7/2012, Period C 3/1/2000-6/7/2012.

Hypotheses H 0 : unit root, H 1 : no unit root (stationary).


Table 6 Results of the ADF Unit Root Tests (First Differ-

enced Series)

tau-statistic

Country Index A B C

Sri Lanka CSE ASI −31.9368 −36.7167 −56.4992

Pakistan Karachi-100 −41.3071 −22.0013 −51.7967

Malaysia KLSESI −36.3735 −47.0991 −67.3129

Korea KOSPI −43.5829 −35.6789 −56.5305

Japan Nikkei-225 −44.9859 −36.3785 −57.8759

Philippines PSECI −41.4350 −31.6256 −51.1956

India SENSEX −42.0563 −33.7916 −53.7779

Singapore Straits-Time −43.0580 −36.5828 −57.2228

Taiwan Taiwan WI −23.1837 −34.4915 −55.8142

Notes : Period A 3/1/2000-20/7/2007, Period B 23/7/2007-6/7/2012,

Period C 3/1/2000-6/7/2012.

Hypotheses H 0 : unit root, H 1 : no unit root (stationary). All series

are significant at 1% level.

Figure 1 Behavior of the Stock Market Indices

in their first difference. As we expected, under the PP test the level series were nonstationary and

the first differenced series rejected the null hypothesis of unit root. It is noteworthy that the

application of the PP unit root test led to the same conclusions that we did with the Dickey-Fuller

tests. Therefore, we omit the PP test results.

In the following section, we will employ Johansen cointegration analysis followed by error

correction modeling and finally, if appropriate, Granger Causality tests for short run causality. An

essential characteristic of the dynamics of stock market relations is identified if different stock

price indices follow a common trend and if there is a long run relationship between them. Insights

into these issues are obtained by the cointegration analyses.

Since all stock indices are I (1), the Johansen cointegration test is conducted for the level data.

According to Johansen (1988) and Johansen and Jusellus (1990), there are two types of test for

cointegrating vectors, namely (i) the trace test, (ii) the Maximum Eigen value test. Both tests

identify several equilibrium relationships governing the joint evolution of all the variables. Using

trace statistic and Maximum Eigenvalue statistic, the sequential procedure is done to find the

number of cointegration relations. Table 7 provides the results from both trace and Maximal

Eigenvalue test. We found codintegration relations only for the period B from 23/7/2007 to

6/7/2012 summarized in Table 7. The Trace statistic for this period indicates that there are four

cointegrating equations at the 5% level and Maximum Eigenvalue test indicates 3 cointegrating

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Figure 2 Behavior of First Difference of the Indices

equations at the 5% level.

The existence of long-run relationship in the post economic crisis period leads to an absence of

long term diversification benefits when we invest funds across the national stock markets. Furth-

ermore, the increase of the number of cointegrating vectors within this particular system indicates

that the current economic crisis had an effect on strengthening stock market relations. The

results demonstrate that stock prices in those countries share a common trend. In other words, by

and large all the stock indices are moving together in the long-run and the national stock markets

have become more interdependent in the post economic crisis period. This is because of the

increasing liberalization and globalization of financial markets.

Table 7 shows the results from the cointegration analysis in the preferred VAR model for the

different countries. We can see that the results are quite mixed. Somewhat surprisingly, there

does not seem to be a cointegrating relationship in the period A and in the whole period. There-

fore, there is no information on a long-run relationship in these periods. This means, however, that

we can carry on the causality analysis in a VAR framework using the first differences of indices.

Since there are cointegration relations between the stock indices for the period B, the adequate

method to capture short run dynamics is VECM. We employed VECM to the data to identify

causal link for the periods B. Results are summarized in Tables 8, 9, and 10 with F-statistic or Chi-

sq statistic in the first raw and probability in brackets. It is noted that the appropriate lag length

was selected through the use of AIC, while the use of SIC gave similar results.

Table 9 presents the VEC results for the period B, and Tables 8 and 10 the VAR results in first-


Table 7 Cointegration Test Results (Trace and Eigenvalue Statistic)

Period B

HypothesizedNo. of CE(s)

TraceStatistic

CriticalValue

**

Max-EigenStatistic

CriticalValue

**

None 263.9049*

197.3709 63.09667*

58.43354

At most 1 200.8083*

159.5297 53.8523*

52.36261

At most 2 146.9559*

125.6154 47.67218*

46.23142

At most 3 99.28377*

95.75366 37.53167 40.07757

At most 4 61.7521 69.81889 29.24202 33.87687

At most 5 32.51009 47.85613 17.00825 27.58434

At most 6 15.50184 29.79707 7.635653 21.13162

At most 7 7.866184 15.49471 4.80043 14.2646

At most 8 3.065754 3.841466 3.065754 3.841466

Notes : Trace test indicates 4 cointegrating equations at the 5% level and

Maximum Eigenvalue test indicates 3 cointegrating equations at the

5% level. Cointegration test are computed using regressions with inter-

cept and trend terms.*denotes rejection of the hypothesis at the 5% level**MacKinnon-Haug-Michelis (1999) p-values

第 14 巻第１号152

Table 8 Pairwise Granger Causality Test Results (VAR Model) in the Period A

Country Japan Korea India Malayasia Pakistan Philippines Singapore Sri Lanka Taiwan Causes

Japan ―0.25643

(0.7738)1.20377

(0.3003)0.31242(0.7317)

1.92206(0.1466)

2.21036(0.1099)

2.11322(0.1211)

0.18396(0.832)

8.53748(0.0002)

* 1

Korea8.67673

(0.0002)* ―

2.64913(0.071)

1.45886(0.2328)

4.33985(0.0132)

*

8.17564(0.0003)

*

0.18966(0.8273)

2.10222(0.1225)

10.5672(3E-05)

* 4

India12.3099(5E-06)

*

10.0502(5E-05)

* ―2.52285(0.0805)

0.28202(0.7543)

2.61002(0.0738)

3.36204(0.0349)

**

0.34888(0.7055)

0.28004(0.7558)

3

Malayasia0.60208

(0.5478)1.24796

(0.2873)0.63089

(0.5322)―

0.00631(0.9937)

3.06425(0.0469)

**

3.71877(0.0244)

**

1.46164(0.2321)

3.1889(0.0414)

** 3

Pakistan0.63896

(0.528)0.26354

(0.7684)5.16978

(0.0058)*

1.56778(0.2088)

―0.12836

(0.8795)0.38324

(0.6817)0.21835(0.8039)

0.13956(0.8697)

1

Philippines0.2386

(0.7878)1.57571

(0.2071)12.8487(3E-06)

*

0.78834(0.4547)

0.35404(0.7019)

―1.85948

(0.156)0.25235(0.777)

5.30092(0.0051)

* 2

Singapore15.0172(3E-07)

*

3.96697(0.0191)

**

4.29016(0.0138)

**

2.46795(0.085)

3.8076(0.0224)

**

13.9173(1E-06)

* ―0.42138(0.6562)

15.2154(3E-07)

* 6

Sri Lanka0.165

(0.8479)0.19084

(0.8263)1.80281

(0.1651)0.32255(0.7243)

0.58431(0.5576)

0.23826(0.788)

0.48884(0.6134)

―0.11705

(0.8895)0

Taiwan2.76728

(0.0631)1.1042

(0.3317)17.994(2E-08)

*

7.17395(0.0008)

*

1.0678(0.344)

6.35029(0.0018)

*

2.53634(0.0794)

1.7396(0.1759)

― 3

Caused 3 2 4 1 2 4 2 0 5 23

Values in brackets are p-values. Superscripts indicate significance at :*: 1% level and

**: 5% level.

Table 9 VEC Granger Causality/Block Exogeneity Wald Test Results in the Period B


Japan ―8.7177

(0.0128)**

6.9320(0.0312)

**

2.2777(0.3202)

2.8142(0.2449)

3.1017(0.2121)

2.2044(0.3321)

0.4257(0.8083)

2.4318(0.2964)

2

Korea0.5057

(0.7766)―

7.5734(0.0227)

**

3.0445(0.2182)

0.4217(0.8099)

11.884(0.0026)

*

0.2993(0.8610)

6.1107(0.0471)

**

3.5632(0.1684)

3

India37.8533(0.0000)

*

26.9921(0.0000)

* ―22.1016(0.0000)

*

0.3441(0.8420)

38.2868(0.0000)

*

6.4608(0.0395)

**

8.0400(0.0180)

**

33.3619(0.0000)

* 7

Malayasia8.7496

(0.0126)**

7.7983(0.0203)

**

0.4487(0.7990)

―1.9122

(0.3844)1.8923

(0.3882)5.1576

(0.0759)1.0690

(0.5860)2.3493

(0.3089)2

Pakistan0.8770

(0.6450)2.9062

(0.2338)0.0944

(0.9539)1.1344

(0.5671)―

1.2748(0.5287)

0.5610(0.7554)

6.8014(0.0333)

**

3.2906(0.1930)

1

Philippines0.6795

(0.7120)2.3535

(0.3083)1.4942

(0.4737)20.0247(0.0000)

*

0.0068(0.9966)

―0.4651

(0.7925)0.0895

(0.9562)1.0893

(0.5800)1

Singapore28.322(0.0000)

*

19.3630(0.0001)

*

0.9764(0.6137)

9.2407(0.0098)

*

0.8027(0.6694)

14.5141(0.0007)

* ―0.5855

(0.7462)11.6046(0.0030)

* 5

Sri Lanka3.1976

(0.2021)0.7512

(0.6869)1.7977

(0.4070)1.7320

(0.4206)0.8907

(0.6406)0.5054

(0.7767)1.2383

(0.5384)―

2.0646(0.3562)

0

Taiwan7.2725

(0.0264)**

1.9665(0.3741)

0.9404(0.6249)

0.2122(0.8993)

1.3704(0.5040)

9.0836(0.0107)

**

1.4159(0.4927)

2.4695(0.2909)

― 2

Caused 4 4 2 3 0 4 1 3 2 23


**: 5% level.

differences for the period A and for the whole period, respectively.

The results of Granger causality test shown in Table 10 indicate that among these markets,

thirty eight significant causal relationships are found at the 5% level or less than that. For

example, Malaysian market (column 4) shows that Japan, Korea, India, Pakistan, the Philippines,

Singapore and Taiwan markets affect Malaysian market. Table 8 indicates that Malaysian market

is influenced by only Taiwan. Not surprisingly, the Singapore market is one of the most influential

markets in the Asian regional area, influencing Japan, Korea, India, Pakistan, the Philippines and

Taiwan. The least influential market in the pre-crises period is Sri Lanka.

The results of period B summarized in Table 9 indicate that India Granger-causes seven other

markets in the post-crises period and eight other markets over the total sample period as shown in

Table 10. While Korea and Singapore Granger-cause seven other markets over the total sample

period, India became the most influential market in the region. One important feature in the pre-

and post-crisis period is that the total number of causal links is twenty three. The relative

influence of the Indian market has increased substantially in the post-crises period. Second largest

influential markets in the whole sample period are Korea and Singapore. Furthermore, one of the

most interesting findings is the change of the most influential markets, as measured by causal

relations, in the post-crisis period as compared to the pre-crisis period. In the pre-crisis period,

Korea and Singapore account for ten of the twenty three significant causal relationships. In the

post-crisis period these markets account for fewer significant causal relationships, and India has

increased its relative importance up to seven. Another finding is that in the period B Sri Lankan


Table 10 Pairwise Granger Causality Test Results (VAR Model) in the Whole Period


Japan ―0.8972

(0.4078)1.0462(0.3514)

11.6382(9.00E-06)

*

6.30506(0.0018)

*

9.41143(8.00E-05)

*

4.31058(0.0135)

**

1.21876(0.2957)

7.47943(0.0006)

* 5

Korea12.0356(6E-06)

* ―3.6366(0.0264)

**

24.665(2E-11)

*

7.67424(0.0005)

*

30.3024(9E-14)

*

0.84524(0.4295)

5.14651(0.0059)

*

11.6384(9E-06)

* 7

India55.5617(2E-24)

*

33.2041(5E-15)

* ―37.5343(8E-17)

*

6.12223(0.0022)

*

64.4761(3E-28)

*

6.16299(0.0021)

*

6.75969(0.0012)

*

49.3646(8E-22)

* 8

Malayasia1.293

(0.2746)1.32825

(0.2651)0.55799(0.5724)

―1.01099(0.364)

5.27752(0.0051)

*

4.2275(0.0147)

**

2.40214(0.0907)

2.50389(0.0819)

2

Pakistan1.42656

(0.2403)0.97064

(0.379)0.33055(0.7185)

3.39978(0.0335)

** ―0.94687

(0.3881)0.32128(0.7252)

0.05621(0.9453)

1.21789(0.296)

1

Philippines0.48627

(0.615)3.41348

(0.033)**

3.44883(0.0319)

**

12.9783(2.00E-06)

*

1.32625(0.2656)

―0.79029(0.4538)

1.21555(0.2967)

6.19103(0.0021)

* 4

Singapore51.1316(1E-22)

*

19.9692(2E-09)

*

5.18089(0.0057)

*

42.7895(5E-19)

*

8.4439(0.0002)

*

59.1069(6E-26)

* ―1.35759(0.2574)

35.7181(5E-16)

* 7

Sri Lanka2.65555

(0.0704)0.11615

(0.8903)1.81336(0.1633)

1.37415(0.2532)

1.56753(0.2087)

0.63552(0.5297)

1.39963(0.2468)

―0.75213

(0.4714)0

Taiwan2.65787

(0.0702)1.58181

(0.2058)1.2969

(0.2735)22.0486(3E-10)

*

3.44328(0.0321)

**

15.3064(2E-07)

*

4.23058(0.0146)

**

1.58588(0.2049)

― 4

Caused 3 3 3 7 5 6 4 2 5 38


**: 5% level.

market is influenced by Korea, India and Pakistan. Interestingly, Table 8 shows that none of the

stock markets under examination have a significant causal relationship with Sri Lanka before the

economic crisis. Specially, Pakistan also reported similar results after the crisis period.

In fact, the Sri Lankan stock market acts completely through its internal dynamics during this

period (see Figure 3). This result is consistent with the study by Elyasiani et al. (1998) who

considered the period 1989-1994. This lack of interdependency in the Sri Lankan stock market

implies additional profit opportunities and diversification benefits to global investors.

第 14 巻第１号154

Figure 3 Impulse Response of Sri Lanka from other Stock Exchanges in the Period B

5 Conclusion

This paper examines price relationships among nine East and South Asian stock exchanges in

the period 2000-2012. First, the ADF and PP tests were applied to the data to identify stationary

properties of the indices. The first-differenced series rejected the null hypothesis of unit root,

indicating that they were stationary for all periods. Consequently, all nine series were integrated I

(1). Next, we employed Johansen cointegration analysis followed by error correction modeling,

and finally we applied Granger tests for short run causality. We found cointegration relations only

for the period B from 23/7/2007 to 6/7/2012. We can see that the results are quite mixed.

Somewhat surprisingly, there does not seem to be a cointegrating relationship in the periods A

3/1/2000-20/7/2007 and in the whole period 3/1/2000-6/7/2012.

The above result indicates that the current economic crisis had an effect on strengthening

stock market relations by increasing the number of cointegrating vectors within this particular

system. At the next step, we employed VECM to the data to identify causal link in the period of

23/7/2007-6/7/2012.

The result shows that, Singapore market is one of the most influential markets in the Asian

regional area, influencing Japan, Korea, India, Pakistan, Taiwan and the Philippine. The least

influential market in the pre-crises period is Sri Lanka. Meanwhile, the results show that India

Granger-causes seven other markets in the post-crises period and eight other markets over the

total sample period. While Korea and Singapore Granger-cause seven other markets over the

whole period, Indian became the most influential market in the region. The relative impact of the

Indian stock market has increased substantially in the post-crises period. Second largest influen-

tial markets in the whole sample period are Korea and Singapore. In fact, Sri Lankan stock market

acts completely through its internal dynamics during the pre economic crisis period. This result is

consistent with the study by Elyasiani et al. (1998) who considered the period 1989-1994.

Not surprisingly, the leading role of Singapore market in the Asian region is clearly visible

throughout all causality tests for every time periods. One of the most interesting finding is that Sri

Lankan stock market started to be Granger caused in the post-crisis period.

The main implication of this results is that under the context of globalization, the stock markets

are enhanced their interrelations after the recent economic crisis. Especially an emerging market

of Sri Lanka is affected by other markets in the post economic crisis. Possible reasons for this

trend in Sri Lanka are trade and foreign investment interaction, increasing liquidity in the stock

exchange after the civil conflict, universal process of microeconomic reforms following from the

economic crises themselves.

South East Asia as a region has undergone rapid market liberalization in the recent years,

which results enhanced investment opportunities. This financial openness increases the causal

linkages between countries. In particular, world financial crisis (2007) has changed the direction

and strength of the causal relationships among the markets under study. In the pre-crisis period


most Asian equity markets were relatively isolated from each other or were subject to only a few

direct causal links.

References

Bracker K., D. S. Docking, and P. D. Koch (1999), Economic Determinants of Evolution in International Stock

Market Integration, Journal of Empirical Finance, Vol. 6, pp. 1-27

Chung and Ng (1991), Interaction Between the US and Japan Stock market Indices, Journal of International

Financial Markets, Vol. 2(2), pp. 51-69

Cheung, Y. L. and S. C. Mak (1992) The International Transmission of Stock Market Fluctuation between the

Developed Markets and the Asian-Pacific Markets, Applied Financial. Economics, Vol. 2, pp. 43-47

Daly, K. J. (2003). Southeast Asian Stock Market linkages : Evidence from Pre- and Post-October 1997. ASEAN

Economic Bulletin, Vol. 20(1), pp. 73-85

Elyasiani E., P. Perera, and T. N. Puri. (1998), Interdependence and Dynamic Linkages between Stock Markets of

Sri Lanka and Its Trading Partners, Journal of Multinational Financial Management, Vol. 8, pp 89-101

Eun C. S. and S. Shim (1989), International Transmission of Stock Market Movements, Journal of Financial and

Quantitative Analysis, Vol. 24(2), pp. 241-256

Gklezakou T. and J. Mylonakis (2009), Interdependence of the Developing Stock markets, Before and During the

Economic Crisis : The Case of South Europe, Journal of Money, Investment and banking, Vol. 11, pp 70-78

―― (2010), Links and Interdependence of Developed Stock Markets under Global Economic Crisis Conditions,

Journal of Financial Services Marketing, Vol. 14(4), pp. 314-327

Glezakos M., A. Merika, and H. Kaligosfiris (2007), Interdependence of Major World Stock Exchanges : How is the

Athens Stock Exchange Affected ? International Research Journal of Finance and Economics, Vol. 7, pp 24-39

Granger, C. W. (1969) , “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods”

Econometrica, Vol. 37(3), pp. 422-438

Johansen, S. (1988), “Statistical Analysis of Co-integration Vectors”, Journal of Economic Dynamics and Control,

Vol. 12, pp. 231-254

Johansen, S and K. Juselius (1990) , “Maximum Likelihood Estimation and Inference on Co-integration : with

Applications to the Demand for Money”, Oxford Bulletin of Economics and Statistics, Vol. 52, pp. 169-210

Kanas A. (1998), Linkages between US and European Equity Markets, Applied Financial Economics, Vol. 8, pp

607-614

Karim, M. Z. A., & Gee, C. S. (2006), Stock Market Integration between Malaysia and its Major Trading Partners

(1994-2002), Applied Econometrics and International Development, Vol. 6(3), pp. 203-224

Kwan A. C. C., Sim A. B., and Cotsomitis J. S. (1995) , The Causal Links Between Equity Indices on World

Exchanges, Applied Economics, Vol. 27(1), pp. 33-37

LU K., M. Sriyalatha, C. Kiran, M. Kunimura and H. Torii (2012), The Interdependence of Stock Markets between

India and neighboring Countries, Sri Lanka and Nepal, Meijo Asian Research Journal, Vol. 3(1), pp 53-56

Malliaris A. G. and J. L. Urrutia (1992), The International Crash of October 1987 : Causality Tests, Journal of

Financial and Quantitative Analysis, Vol. 27, pp 353-364

Majid, M. S. A, Meera, A. K. M., & Omar, M. A. (2008), Interdependence of ASEAN-5 Stock Markets from the US

and Japan, Global Economic Review, Vol. 37(2), pp. 201-225

Mansor H Ibrahim, (2005), International Linkages of Stock Prices : The case of Indonesia. Management Research

News, Vol. 28(4), pp. 93-115

Mukherjee, P. and Bose, S. (2008), “Does the stock market in India move with Asia ? A multivariate cointegration-

第 14 巻第１号156

vector autoregression approach”, Emerging Markets Finance & Trade, Vol. 44(5), pp 5-22

Roca E. D. (1999), Short-term and Long-term Price Linkages between the Equity Markets of Australia and its

Major Trading Partners, Applied Financial Economics. Vol. 9, pp. 501-511

Sims, C. (1972), Money, Income and Causality, American Economic Review, Vol. 62, pp. 540-552

Sriyalatha M. A. K., Torii, H., and Kunimura M. (2012), Interdependence of the Stock Markets, Pre and Post Asian

Crisis and Economic Recession : How is the Sri Lankan Stock Exchange Affected ? The Meijo Review, Vol. 13,

no. 2, pp. 19-38

Worthington A., M. Katsuura, and H. Higgs (2003), Price Linkages in Asian Equity Markets : Evidence Bordering

the Asian Economic, Currency and Financial Crises, Asia-Pacific Financial Markets, Vol. 10(1), pp. 29-44

Yusof, R. M. & Majid, M. S. A. (2006), Who moves the Malaysian Stock Market - the US or Japan ? Empirical

evidence from the pre-, during, and post-1997 Asian Financial Crisis, Gadjah Mada International Journal of

Business, Vol. 8, pp. 367-406


Interdependence of the Stock Markets, Before andDuring the Economic Crisis :The Case of East and South AsiaAbstract1 Introduction2 Literature Review3 Data and Methodology3.1 Data3.2 Methodology

4 Preliminary Analysis and Empirical Results4.1 Preliminary Analysis4.2 Empirical Results

5 Conclusion

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