Interlinkage of the International Stock Markets · empirical studies that examined the various...

FOCUS: Journal of International Business

Volume 6, Issue 1, January-June, pp. 152-168

doi: 10.17492/focus.v6i1.182826

Interlinkage of the International Stock Markets

Pardeep Kumar*, Charu Saxena** and Mandeep Kaur***

ABSTRACT

Capital market investors devoid of sufficient investment opportunities in the domestic

market look forward to investment in the global markets, to seek the additional benefit

of the global diversification. Globalization, liberalization, financial reforms, swelling

multilateral relations among the world countries, massive development in the

information technology and communication systems and ever-rising trade between the

nations, signify the integration of the world markets especially the capital markets. This

research work focuses on examining the Interlinkage between the stock markets of the

world by applying the Johansen (1988) cointegration framework on the world’s twelve

renowned stock price indices belonging to India, US, UK, Singapore, France, Japan,

Germany, Brazil, Russia, China, Hong-Kong, and Canada. The study could not validate

the strong presence of the interlinkage among the stock markets of the world.

Keyword: Capital market; Cointegration; Diversification; Interlinkage.

1.0 Introduction

Markets all over the world are integrating and interconnecting at a very rapid

pace. The effect of globalization on international economic activities is evidently

visible. Famous economist Thomas Friedman said, ―The world is flat‖ and meant that

the economic activity and trade among the different countries of the world is increasing

at a rapid pace. This is leading to opening up of the world markets, which together with

improved information and communication technology, upgraded infrastructure, better

supply chain system, increasing impact of the World Trade Organization (WTO) in

reducing the world-trade tariffs, import quotas, and barriers; is instigating integration of

the world economies.

___________________

*Corresponding Author; HOD/Assistant Professor, Continental Group of Institutes, Fatehgrah

Sahib, Punjab, India (E-mail: [email protected])

**Assistant Professor, Govt. Ranbir College, Sangrur, Punjab, India (E-mail:

[email protected])

***Student, CIET, Jalvehre, Fatehgrah Sahib, Punjab, India (E-mail:

[email protected])

Interlinkage of the International Stock Markets 153

The speed and intensification of international economic linkages among nations

are growing. Internationalization of trade and finance; establishment of free trade areas,

new trade agreements, economic blocks, special trade/economic zones and multilateral

treaties; and intensified transparency, regulation, and effectiveness of international

markets; speedy growth of across the border movement of capital, goods, service and

technology and; policy changes among nations to suit the world order through economic

reforms; rising international tourism & sports activities; increasing activities of Multi-

Nation organizations is taking place. The stock markets of the world are also integrating

over the years due to the opening up of the financial markets of the world. These

markets around the world are expanding beyond boundaries with the movement of

finance and funds in different forms including Foreign Direct Investment, Foreign

Institutional (portfolio) Investment, American Depository Receipts (ADRs), Global

Depository Receipts (GDRs), External Commercial Borrowings (ECBs), secured and

unsecured loans, foreign-exchange transactions etc.

The correlation interrelationship, integration and dynamic linkages among

countries of the global stock markets have become increasingly important, and have

become the topic of interest for everyone. The stock market co-integration or inter-

linkage means to identify the presence of long term dependencies across different stock

markets. The growing interest in the field is validated by the existences of several

empirical studies that examined the various aspects of stock market linkages. These

studies were mainly motivated by many big financial happenings in the past. Taking

this further the financial crisis and stock markets meltdown of 2008-2009 can be taken

as the most recent cause to study the Inter-Linkage & interdependence of stock indices

of different stock exchanges of the world countries. The linkages are of great concern

for those who look outside to invest in different countries. International portfolio

diversification helps investors in reducing their portfolio risk because of the existence of

less correlated securities due to different economic, political and institutional issues and

factors prevailing across the different countries. The research is structured as follows.

Firstly, a brief review of literature on cointegration of stock markets around the world is

done; which is followed by a section on the methodology used for analysis. Further, the

empirical results and discussion are summarized and conclusions are presented in the

end.

2.0 Literature Review

The previously available relevant literature relating to stock market integration

has been reviewed in order to establish the research gap and need for the present study.

Some of the prominent studies are as discussed in the following paragraphs:

154 FOCUS: Journal of International Business, Volume 6, Issue 1, Jan-Jun, 2019

Kasa (1992) studied the US, Japan, Germany, Britain, and Canada stock

markets using Johansen's cointegration and concluded the strong evidence for a single

common trend in the markets. Empirical evidence for long-run equilibrium was

observed.

Arshanapalli and Doukas (1993), used co-integration to find whether the

France, Germany, Japan and United Kingdom stock market were interlinked before and

after the 1987 crash. It was confirmed that share markets of only US & Japan were

cointegrated.

In another landmark study on dynamic relationships among the U.S., Japan,

U.K., and German stock markets from 1984 to 1991, both short-run and long-run

relationships were observed. Moreover, the U.S. stock market led other stock markets in

short-run only in the pre-crisis and post-crisis periods of October 1987 crash but led all

the selected markets in the long-run in all periods under the study (Hasan and Naka,

1996).

Kumar and Mukhopadhyay (2002) observed the short-run dynamic

relationships between NSE Nifty and NASDAQ Composite for 1999-2001 and

concluded the presence of unidirectional Granger causality from the U.S. to the Indian

share market.

Hoque (2007) proved that share market prices of Bangladesh, US, Japan, and

India were integrated. In the same year, Phylaktis and Ravazzolo (2007) inspected stock

market relationships of a group of Pacific-Basin countries with that of US and Japan for

1980-1998. Significant stock market integrations were confirmed. In another study the

Mexican stock market was confirmed to be integrated with the US equity markets

(Canarella, 2008).

Heilmann (2010) in his research found the strong influence of the US market on

the Asian stock market for both short and long run periods. It did not advocate many

indications for the long-run relationships between the stock indices.

Patel et al. (2012) in their paper had examined the short-run relationships

among the share markets to analyse co-movement of Indian capital market index with

the developed as well as developing countries’ indices. They interpreted that the Indian

stock market was interdependent of that of the developed countries except for Japan.

Sheu and Liao (2011) studied the Interlinkage and Granger causality relations

amongst the developed i.e. the US and developing i.e. BRIC stock markets. The

observed results recognized that the stock markets of Brazil, Russia, and China had

begun exercising substantial impacts on the US to some extent but US markets still had

a dominant role.


Jagroop (2012) analysed the relationship between the US stock exchange and

Islamic stock exchanges and found the existence of a relation between these exchanges.

It was further concluded that the US stock exchange had an impact on the Islamic

exchanges but the Islamic stock exchanges didn’t have any effect on the US exchange.

Sakthivel and Kamaiah (2012) attempted to investigate the dynamic inter-linkages

among the Asian, European, and U.S. stock markets during 1998 to 2010 and revealed

that the U.S. and some of the European and Asian stock markets lead the Indian stock

markets.

Sharma et al. (2013) examined the inter-linkages between stock markets of

BRICS and established that the stock markets under study were influenced to some

extent by each other but not to a large extent. This implied that there existed good

prospects for diversification of risk for global investors.

Wuthisatian (2014) used daily stock prices during 1997–2013 to investigate the

interlinkage between Thailand and its eleven principal trading partners and confirmed

that the indices of Thailand displayed a feeble long duration bond with the other

selected markets. Further in another study, inter-linkages between the equity markets of

India, Indonesia, Thailand, Malaysia and Korea with the US were concluded (Kalsie,

2014).

Singh & Shrivastav (2017) examined the financial integration between stock

Market of India and Australia. It was concluded that both the markets did not Granger

cause returns at each other. The Johansen test confirmed the absence of any significant

co-integration between these equity markets.

Ben (2018) examined the cointegration, dynamic linkage and portfolio

diversification in African stock markets during 2004-2016 by using Johansen

cointegration test and concluded the nonexistence of a cointegration relationship

between the Nigerian market and the considered stock markets.

In another extensive study Khandelwal and Singh (2018) an attempt to analyze

linkages between stock markets of India, Indonesia, Philippines, and Taiwan is made by

using the monthly data for the period 2000-17. The results of the Johansen test

confirmed the existence of long-run linkages among the selected equity markets,

whereas VECM results concluded the presence of long causality from the selected

markets to Indian markets.

Gulzar et al. (2019) examined the financial Interlinkage and spillover effect of

the global financial crisis to evolving Asian markets (China, Pakistan, Malaysia, India,

Russia and Korea) using the daily stock returns during 2005 to 2015 and concluded the

substantial shock spillover and volatility spillover effects of the global financial crisis

from the New York Stock Exchange to the various emergent Asian share markets.


The above review of the literature reveals that there are not many studies

focused on the selected stock markets of the world including both developing &

developed economies. Also, there is a dearth of research on the co-integration/inter-

linkages of stock indices before and after the financial crisis of 2008-09. Therefore the

present study attempts to fill this research gap.

3.0 Methodology

The present study focuses on the Cointegration or inter-linkages between key

stock markets of the world. With the global economies becoming more inter-linked

financially, it needs to be seen how many financial inter-linkages are there among these

economies vis. US, UK, Singapore, France, Japan, Germany, Brazil, Russia, India,

China, Hong-Kong, and Canada using the benchmark indices of their prominent stock-

exchanges. S&P BSE Sensex (Bombay Stock Exchange, India), Dow 30 (New York

Stock Exchange - NYSE, US), FTSE 100 (London Stock Exchange, UK), STI

(Singapore Exchange-SGX, Singapore), CAC 40 (Euronext Paris, France), NIKKEI 225

(Tokyo Stock Exchange, Japan), DAX (Frankfurt Stock Exchange, Germany), ibovespa

(BM&F BOVESPA-Sao Paulo Stock Exchange, Brazil), MICEX (Moscow Interbank

Currency Exchange MICEX, Russia), SSE Composite Index (Shanghai Stock

Exchange, China), HSI (Hong Kong Stock Exchange – HKEx, Hong-Kong), and

S&P/TSX 60 (Toronto Stock Exchange-TSX, Canada), are the selected stock indices.

Daily stock market indices values for each of these indices are used for a period of

twelve years from 2004.

Cointegration is defined as a situation where linear combinations of non-

stationary time series are stationary. This leads to the presence of long duration

equilibrium between the variables. So, before applying the co-integration, series are

required to be non-stationary and integrated of order one. Augmented Dickey-Fuller

unit root test is conducted to confirm the stationary nature of the series. After this the

Johansen Test (Johansen and Juselius, 1990) is used to ascertain the presence of the

cointegration among the stock markets of the selected countries.

4.0 Empirical results and discussion

4.1 Descriptive return statistics

The various important statistics about the return data are summarized in Table

1. Table 1 provides summary statistics, such as the sample mean-return, minimum,

standard deviation, skewness, and the Kurtosis values, for the selected indices return


series. From the table it can be inferred that the daily mean returns for most of the

indices are positive and the stock exchanges of India, China, Singapore, Brazil,

Germany & Canada have shown higher average daily returns which are 0.0414%,

0.0373%, 0.0311%, 0.0305%, 0.0299%, 0.000234% respectively whereas the stock

exchanges of France, United Kingdom, Russian, Japan, United States & Hong Kong

have shown inferior average daily returns which happen to be 0.0048%, 0.0052%,

0.0101%, 0.0127%, 0.0188%, 0.0214% respectively.

Table 1: Descriptive Return Statistics

S. No. Variable Mean Return Standard Deviation Skewness Ex. Kurtosis

1 rIndia 0.000414 0.0166743 -0.0969 7.32

2 rJapan 0.000127 0.0155072 -0.3847 8.54

3 rFrance 0.000048 0.0149508 0.0257 5.60

4 rGermany 0.000299 0.0148588 -0.1520 6.38

5 rBrazil 0.000305 0.0202475 -0.2717 5.41

6 rSingapore 0.000311 0.0119093 -0.0648 8.63

7 rUK 0.000052 0.0122733 -0.0941 7.75

8 rChina 0.000373 0.015954 -0.2525 3.97

9 rCanada 0.000234 0.0125402 -0.7460 10.05

10 rHong-Kong 0.000214 0.0151624 0.0408 10.40

11 rRussia 0.000101 0.0234506 -0.1361 13.75

12 rUS 0.000188 0.0112492 -0.0720 11.98

By looking at the standard deviation figures it can be concluded that Russia

Singapore (2.35%), Brazil (2.02%), India (1.67%), China (1.60%), Japan (1.55%) &

Hong-Kong (1.52%) have been the most volatile markets whereas US(1.12%),

Singapore(1.19%), UK(1.23%), Canada(1.25%), Germany(1.49%), & France (1.5%)

have been less volatile during the period. Skewness data lies within +/- 1 range for all

the stock indices which shows that the data distributions are nearly symmetric. Also, the

Kurtosis statistic for all the stock exchanges is higher than 3, which proves that the

distributions are peaked (Leptokurtic) relative to the normal. So, the distribution of

selected stock indices returns is thick-tailed during the period under study.

4.2 Data Stationarity

To apply co-integration we study the returns data graphs in Figures 1 and 2.

From the different graphs (individual stock indices graph and graph of the group of


indices) we observe that the return data looks to be stationary. Further, the Augmented

Dickey-Fuller (ADF) Test is used for the stationarity of the data.

To check the stationarity of the variables hypothesis is as given

H0: ɸ = 1, Ha: ɸ < 1

Data is taken to be stationary in case the null hypothesis is rejected and it is

non-stationary, otherwise. This non-stationarity is being checked by comparing ADF

value with DF critical values i.e. 3.4 and 3.1 for 5 per cent and 10 per cent level of

significance, respectively.

Figure 1: Return of Individual Stock Indices

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

12

62

52

37

84

10

45

13

06

15

67

18

28

20

89

23

50

26

11

rINDIA

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

12

40

47

97

18

95

71

19

61

43

51

67

41

91

32

15

22

39

12

63

0

rJAPAN

-0.15

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0.00

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12

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44

36

64

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02

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3

rFRANCE

-0.15

-0.10

-0.05

0.00

0.05

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0.15

1

28

8

57

5

86

2

11

49

14

36

17

23

20

10

22

97

25

84

rGERMANY


-0.20

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0.00

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1

26

2

52

3

78

4

10

45

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06

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11

rBRAZIL

-0.15

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-0.05

0.00

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1

26

2

52

3

78

4

10

45

13

06

15

67

18

28

20

89

23

50

26

11

rSINGAPORE

-0.15

-0.10

-0.05

0.00

0.05

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0.15

1

26

2

52

3

78

4

10

45

13

06

15

67

18

28

20

89

23

50

26

11

rUK

-0.15

-0.10

-0.05

0.00

0.05

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1

26

2

52

3

78

4

10

45

13

06

15

67

18

28

20

89

23

50

26

11

rCHINA

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15rCanada

-0.15

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0.00

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12

22

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64

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51

10

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32

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02

21

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43

22

65

3

rHONGKONG


Source: Return Data as compiled from stock indices historical figures

Figure 2: Return of the Group of Indices

Source: Return Data as compiled from stock indices historical figures

As shown in Table 2, the test static values are less than the critical value of 3.1

for 5 per cent level of significance, it means the null hypothesis is accepted which

implies that the natural log data of different stock markets under study are non-

stationary. Then, the ADF test is applied on the first order difference i.e. returns data

and results are as shown in Table 3. Since the entire test static values (absolute) are

more than the critical value of 3.1 for 5 per cent level of significance, it means the null

-0.30

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0.00

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0.30

12

06

41

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16

82

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02

61

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43

61

64

11

84

62

05

12

25

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46

12

66

6

rRUSSIA

-0.10

-0.05

0.00

0.05

0.10

0.15

1

24

0

47

9

71

8

95

7

11

96

14

35

16

74

19

13

21

52

23

91

26

30

rUS

-0.30

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-0.10

0.00

0.10

0.20

0.30

11

12

22

33

34

44

55

56

66

77

78

88

91

00

01

11

11

22

21

33

31

44

41

55

51

66

61

77

71

88

81

99

92

11

02

22

12

33

22

44

32

55

42

66

52

77

6

rINDIA rJAPAN rFRANCE rGERMANY

rBRAZIL rSINGAPORE rUK rCHINA

rCanada rHONGKONG rRUSSIA rUS


hypothesis is rejected which implies that the return data of different stock markets under

study are stationary. From the above analysis, it can be inferred that data itself is non-

stationary but data of order 1 is stationary. This satisfies the necessary condition to

conduct cointegration tests.

Table 2: ADF Test for Natural Log Data

S. No. Country Ln Data ADF Test Static p-value

1. ln India 0.940931 0.9083

2. ln France 0.11974 0.7205

3. ln Germany 0.885235 0.8996

4. ln Canada 0.853085 0.8943

5. ln China 1.16926 0.9381

6. ln Brazil 0.61411 0.849

7. ln Japan 0.483406 0.8195

8. ln United Kingdom 0.254435 0.76

9. ln Singapore 0.928361 0.9064

10. ln Hong-Kong 0.682376 0.8631

11. ln USA 1.11826 0.9322

12. ln Russia 0.0173644 0.6883

Table 3: ADF Test for Returns Data

S.

No.

Country

(Return Data)

ADF

Test Static p-value

1. rIndia -9.57624 2.745e-018 Approx. = 0

2. rFrance -24.8513 8.236e-030 Approx. = 0

3. rGermany -9.26463 1.931e-017 Approx. = 0

4. rCanada -12.2328 1.914e-025 Approx. = 0

5. rChina -25.0579 3.34e-041 Approx. = 0

6. rBrazil -8.65807 8.402e-016 Approx. = 0

7. rJapan -13.9726 8.236e-030 Approx. = 0

8. rUK -23.2233 8.756e-042 Approx. = 0

9. rSingapore -11.2285 8.89e-023 Approx. = 0

10. rHong-Kong -14.73 1.431e-031 Approx. = 0

11. rUS -13.1491 8.575e-028 Approx. = 0

12. rRussia -8.83456 2.816e-016 Approx. = 0

4.3 Return correlation

Different correlation coefficients between the returns data are calculated using

the observations as shown in Table 3. From Table 4, it is clear that UK market is highly


correlated with that of France. Also, Hong Kong & Singapore have high values of

correlation. All other correlations are insignificant. This confirms the existence of not

much of cointegration between the world stock markets.

4.4 Johansen cointegration test results

Johansen test trace statistics pairwise between the different countries’ stock

indices are calculated as given below in Table 5. From Table 5 it can be inferred that

since the test statistic in case of pairwise testing of India & Japan i.e. 20.166 is more

than the critical value i.e. 15.49471, so the Indian & Japanese stock markets could be

said to be cointegrated or interlinked. Similarly, it can be said that the Indian markets

are interlinked with that of Singapore, Germany, Brazil, Russia, China, Hong-Kong, and

Canada but not with France, US, and the UK. Japan is found to be interlinked with all

the selected stock markets except that of Brazil & Singapore. France got the

Interlinkage only with Japan. Further, Germany’s stock market depicted cointegration

with Singapore, Hong-Kong, India and Canada only.

Table 4: Correlation Between Stock Indices’ Return

rIn

dia

rJap

an

rFra

nce

rGer

man

y

rBra

zil

rSin

gap

ore

rUK

rCh

ina

rCan

ada

rHK

rRu

ssia

rU

S

Co

un

try

1 .008 .060 0.094 0.058 0.058 -0.004 0.017 0.056 0.050 -0.009 0.068 rIndia

1 .010 0.009 -0.008 -0.004 -0.006 -0.002 0.004 -0.020 -0.024 -0.034 rJapan

1 0.212 0.048 0.374 0.774 0.012 0.096 0.339 0.351 0.125 rFrance

1 0.098 0.065 0.034 0.023 0.137 0.037 0.022 0.152 rGermany

1 0.030 -0.006 -0.004 0.118 0.023 0.047 0.048 rBrazil

1 0.412 -0.016 0.045 0.681 0.259 0.059

rSingapor

e

1 0.005 0.019 0.371 0.382 0.069 rUK

1 -0.031 -0.026 -0.016 0.004 rChina

1 0.021 0.037 0.100 rCanada

1 0.260 0.010 rHK

1 0.112 rRussia

1 rUS

*5% critical value (two-tailed) = 0.0366 for ‘n’ = 2862

Brazil with Singapore, Russia, Hong-Kong, India, and Canada; Singapore with

Hong-Kong, India, Germany, Brazil and Canada; UK with US, Canada and Japan;


China with Hong-Kong, Japan and India; Canada with Hong-Kong, Russia, UK,

Singapore, Brazil, Germany, Japan and India; Hong-Kong with Russia, Canada, China,

Singapore, Brazil, Germany, Japan and India; Russia with Hong-Kong, India, Brazil,

Japan and Canada; and US only with the UK and Japan stock markets.

Table 5: Johansen Cointegration Test Results

Country I Country II Eigen Value Trace Statistic Prob.

India

Japan 0.0059225 20.166 0.0080

France 0.0024914 10.932 0.2193

Germany 0.0057629 18.842 0.0136

Brazil 0.0068315 23.928 0.0017

Singapore 0.0058856 20.051 0.0084

UK 0.0037069 13.896 0.0851

China 0.0053520 18.537 0.0154

Canada 0.0078838 25.812 0.0007

Hong Kong 0.019594 59.526 0.0000

Russia 0.0061224 23.109 0.0024

U.S. 0.0017840 5.8920 0.7108

Japan

France 0.010883 36.485 0.0000

Germany 0.0074872 23.945 0.0017

Brazil 0.0030703 12.824 0.1218

Singapore 0.0064535 12.824 0.0045

UK 0.010074 32.582 0.0000

China 0.0063993 20.573 0.0068

Canada 0.010220 33.927 0.0000

Hong Kong 0.0069262 24.311 0.0014

Russia 0.0040835 17.270 0.0251

U.S. 0.013069 39.372 0.0000

France

Germany 0.0019872 8.3820 0.4327

Brazil 0.0018736 8.9496 0.3769

Singapore 0.0015471 7.3117 0.5484

UK 0.0028010 10.984 0.2159

China 0.0036354 13.711 0.0906

Canada 0.0030059 12.707 0.1265

Hong Kong 0.0021245 10.990 0.2155

Russia 0.0021397 11.169 0.2044

U.S. 0.0024361 8.3073 0.4404

Germany

Brazil 0.0026649 11.054 0.2115

Singapore 0.0078142 24.820 0.0011

UK 0.0027844 10.260 0.2660

China 0.0032617 11.406 0.1903

Canada 0.0093886s 29.590 0.0001

Hong Kong 0.014878 45.318 0.0000

Russia 0.0022445 11.955 0.1608

U.S. 0.0040509 12.862 0.1203

Brazil Singapore 0.0048574 18.122 0.0181


UK 0.0026899 11.112 0.2079

China 0.0019829 9.5954 0.3190

Canada 0.0065121 22.894 0.0026

Hong Kong 0.0079608 27.246 0.0004

Russia 0.0041159 16.365 4.5609

U.S. 0.0033392 12.097 0.1538

Singapore

UK 0.0025552 10.434 0.2532

China 0.0027274 11.038 0.2125

Canada 0.013815 43.085 0.0000

Hong Kong 0.0063936 21.739 0.0042

Russia 0.0024495 12.691 0.1272

U.S. 0.0032703 10.176 0.2723

UK

China 0.0031176 10.453 0.2518

Canada 0.0058331 21.411 0.0048

Hong Kong 0.0023816 11.994 0.1588

Russia 0.0021987 10.297 0.2632

U.S. 0.0059122 17.819 0.0203

China

Canada 0.0028746 11.013 0.2141

Hong Kong 0.0067951 21.440 0.0048

Russia 0.0030624 13.581 0.0947

U.S. 0.0016254 5.1170 0.7950

Canada

Hong Kong 0.015476 48.239 0.0000

Russia 0.016024 52.773 6.5399

U.S. 0.0031864 10.025 0.2839

Hong Kong Russia 0.0043138 18.204 0.0175

U.S. 0.0044824 13.702 0.0909

Russia U.S. 0.0022683 7.9711 0.4757

*Critical Value is 15.49471

Thus, it can be said that there is the presence of cointegration only between the

stock markets of some of the selected countries but the world stock markets, in general,

can be said to be not much cointegrated or interlinked. The countries exhibit a weak log

run Interlinkage with other selected countries. This poses an opportunity for investors to

invest and diversify their investments geographically. The portfolio managers can also

look forward to the diversification of their asset portfolios.

Interlinkage has been of great importance because a strong association

diminishes the insulation of domestic equity market from any shock from the crisis in

an international stock market. So, even a small shock in any of the domestic equity

market, may spread to the other integrated equity markets. For example, if the portfolio

investors start pulling out their money from one of the countries, then the same will lead

to a flight of capital from other interlinked markets. This can cause the reduction of

availability of credit in the domestic market which in turn would hamper the rate of

production, and growth rate of the economy. Moreover, several other unfavorable

events like unwanted pressure on exchange-rates, reduction of exports and problems


concerning the balance of payments, might take place. But, since our study has

concluded the presence of weak market linkage among the indices, so in the long run,

each country’s stock exchanges will be driven by country-specific factors. The impact

of international shocks or crisis will be less as the globally integrated financial markets

are more disposed to the world financial crisis. The same could explain the little impact

of the US subprime crisis on the Indian economy. If the interdependence would have

been more than there would have been much more devastating effects on the Indian

capital markets.

In case of low integrated equity markets, the investors will also have access to

more risk minimizing opportunities through international diversification across these

equity markets. Thus, it becomes of paramount importance that while diversifying

globally an investor should look not only for the variance and covariance but also the

cointegration between the stock indices. In the short run, the investment decisions based

only upon the covariance might be fruitful in risk reduction. But, in the long run, due

consideration is required to be given to the cointegration or interdependence

relationships between the equity markets. So the equity investors of different selected

countries can achieve benefits from international diversifications. The shareholder can

get higher profit with reduced risk from international investment-portfolios as compared

to domestic portfolios. The portfolio managers can also take decisions on the same

lines. The investors and portfolio managers can also get benefited from arbitrage

activities both in the short and long run. This means they can explore contemporary

trading opportunities in different markets and get the advantage of the price differentials

in different markets. This will, in turn, provide more opportunities for the development

of the share markets of these nations.

From a policy perspective, cointegrated stock markets contribute to the market

efficiency and financial stability, and the market indices are not able to diverge much

from the long term equilibrium. The cointegration among the different stock markets of

the world will also have a bearing impact on the macroeconomic policies of the

countries. This becomes of vital signs during the times of crisis in any of the world

markets and sound policy decisions can be taken based upon the existence of the

cointegration or Interlinkage relationship between the home country and the country in

crisis.

5.0 Conclusion

The study tried to explore the dynamics of co-movement of the world stock

markets. Johansen cointegration test method is utilized to assess the long duration


Interlinkage relationship of the stock markets in the world. The results confirm that

there are only insignificant indications of cointegration among the selected markets i.e.

stock prices do not share a common movement. In most of the cases, the Johansen test

was unsuccessful in finding any cointegration. This means that world stock markets do

not move together. It means, in the long run, each country’s stock exchanges are driven

by country-specific factors. This research outcome has implications for future investors

and portfolio managers looking forward to long term global diversification

opportunities in these countries.

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