THE INTERDEPENDENCE BETWEEN STOCK …euroasiapub.org/wp-content/uploads/2016/09/11ESSAug-3943-1-2.pdf · examined through cointegration analysis and ... (BSE) and National Stock Exchange

International Journal of Research in Economics and Social Sciences (IJRESS) Available online at : http://euroasiapub.org/current.php?title=IJRESS Vol. 6 Issue 8, August , 2016, pp. 120~132 ISSN(o): 2249-7382 | Impact Factor: 6.225 , | Thomson Reuters ID: L-5236-2015

International Journal of Research in Economics & Social Sciences

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THE INTERDEPENDENCE BETWEEN STOCK MARKETS –An Econometric study of BRICS Nations

Dr Neetu Jain

INTRODUCTION The economies of the BRICS countries (Brazil, Russia, India, China and South Africa) have grown at a rapid pace and are becoming increasingly more integrated with the most developed economies in terms of trade and investment. They account for more than a quarter of the world’s land area, more than 40 percent of the world’s population and about 15 percent of global GDP. Goldman Sachs expects the total nominal GDP for the four BRIC countries (excluding South Africa) to reach $128 trillion in 2050, compared to $66 trillion for the G7 countries at that time. The current and potential growth of the BRICS countries also has important implications for the capitalization of their stock markets as well as for their financial dependence with other stock markets. The four BRIC countries are expected to account for 41% of the world’s stock market capitalization by 2030, when China is expected to overtake the United States in equity market capitalization, thus becoming the largest

Keywords Abstract

BRICS Stock Markets, Stationary and Non - stationary, Correlation and Co-integration , Granger Causality

The relationship between the stock markets of developed countries and developing countries has been examined extensively in literature. However, stock markets of BRICS (Brazil, Russia, India, China and South Africa) countries have received little attention separately despite the rapid growth and liberalization among these countries. So, the aim of the study is investigating above relationships both in short and long-run with special reference to India. To investigate the relationships, the researcher has examined the stock indices of BRICS countries and employed the daily closing price data (all converted in to Indian Rupees. This study has used Jarque-Bera test, and ADF for judging the normality and stationarity of the data series. Based on the above results Stock price linkages has been examined through cointegration analysis and the Engel Granger causality test has also been used to study the long term equilibrium relationships

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Author Correspondence BA (Hons),MA(Eco),Mphil,Phd Assistant Professor Bharati Vidyapeeth Institute of Management and Research

International Journal of Research in Economics and Social Sciences (IJRESS) Vol. 6, Issue 8, August 2016 ISSN(o): 2249-7382 | Impact Factor: 6.225




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equity market in the world. Recently, several studies including Liu et al. (2013) and Zhang et al. (2013) among others, have added South Africa into the BRIC group as this country has also a fast-growing economy, experiences rapid financial market development and sophistication, and is globally recognized as a source possessing sophisticated professional services and financial expertise. South Africa is also known as one of the world’s largest producer of some strategic commodities (e.g., gold, platinum, and chrome) which are a critical resource to support domestic and global economic growth. Thus, the presence of South Africa in the BRICS group provides opportunities to establish a dedicated investment strategy in terms of economic diversification opportunities. In a country like India where the stock market is undergoing significant transformation with liberalisation measures and the analysis of the nature of co-integration with other developed and emerging markets would give an idea of the possible gains to be reaped out of the diversification from Indian markets. Presently the two and the most important stock exchanges of India are Bombay Stock Exchange (BSE) and National Stock Exchange (NSE). Bombay Stock Exchange was founded in 1875.. As per the research of September 2012, 5163 stocks of companies are listed under BSE. It is the 11th largest Stock Exchange in the world by market capitalisation as on 31 December 2012.Its indices are BSE SENSEX, BSE Small cap, BSE Mid cap, BSE 500.The second stock exchange i.e. National Stock Exchange is located in Mumbai . It came into existence in 1992. As per records 1635 companies are listed under NSE and its indices are CNX Nifty, CNX Nifty junior and CNX 500. China with a population of over 1.35 billion and capital as Beijing generates a total GDP of $13.374 trillion .Since the introduction of economic reforms in 1978, China has become one of the world’s fastest growing major economies. China has two main stock exchanges. Shanghai stock exchange which came into existence in 26 November 1990 wherein 932 stocks are listed. Its indices are SSE composite and SSE 50. Shenzhen stock exchange which was founded on 1December 1990 and was opened in 1991 where 1420 stocks are listed. Its index is SSE component. Brazil the largest country in both South America and Latin American region has the total GDP OF $2.190 trillion. Its currency is real. It has stock exchange named BM$FBOVESPA founded in august 23, 1890 by Emilio Rangel Pestana . 525 stocks of companies are listed under it. Its index is BOVESPA. Russia officially known as the Russian federation is situated in northern Eurasia .It generates a total GDP of $2.558 trillion. The Russian economy ranks as the eighth largest by nominal GDP and sixth largest by purchasing power parity .Its stock exchange is Moscow interbank currency exchange founded in 1992. It has 550 participating organisations and members. Its index is MICEX-RTS Index. South Africa, a country located at the southern with JSE i.e. Johannesburg stock exchange. Founded in 8December 1887. As per the records 472 stocks of companies are listed under it. The global growth of stock markets and the emerging market boom of BRICS countries have attracted the attention of academics, practitioners, and policy makers. People in today’s world think that their money should be invested in the most flourishing and rapidly changing market where they can expand the size of their investment So, studying the interrelationships, interdependencies, and integration of their stock markets with the dynamic linkages is the most timely endeavor under this study.





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More specifically, this study aims at finding the short and long-run relationships in between the BRICS stock markets. Which of the BRICS markets is influencing, and causing the movement of the benchmark indices of other markets in short-to-long run will also be studied here. The study will also include whether any change in the index of NIFTY will affect the index of Brazil / Russia/ China/ South Africa and their market scenarios or not. To work on the above objectives, this study uses graphical presentations, descriptive statistics (to state the nature and normality of the data series), correlation test results, Augmented Dickey-Fuller (ADF) (Dickey and Fuller, 1979; 1981) tests and Phillips and Perron (1988) tests (to find out the stationary nature or unit root issues), Johansen and Juselius (1990) co integration technique and Engle and Granger (1987) and Granger (1969) causality test. REVIEW OF LITERATURE We are conducting a research to examine that whether there exists a co integration between the stock market of BRICS nations or not i.e. whether any change in one stock market affects the stock markets of other nations or not . For this an attempt is made to review the recent studies related to this topic. Eun and Shim (1989) investigated the transmission of stock markets of Australia, Canada, France, Germany, Hong Kong, Japan, Switzerland, UK and US. Their objective was to find out the speed with which the movement in one market influenced the other and to see whether US played a dominant role or not. It was observed that there were multilateral interactions among the stock markets and US played a dominant role. Becker, Finnerty and Gupta (1990) studied the interpersonal relation between US and Japanese markets using the opening and closing data from 1985 to 1988. The objective was to find out whether the time difference in the trading system can be used for arbitrage or not. It was observed that although high profits were reaped out but this strategy was eliminated due to high transaction cost and taxes. Hamao, Musulis, and Ng (1990) analysed the short term linkages among 3 major stock markets – New York, Japan and London. Their objective was to find out that whether change in one market influences the other market or not and whether they are positively related or not .The results supported the existence of integration among these markets. It was found that spill over effect from Japanese market to the US and the UK market was found to be week. Lee and Kim (1994) examined the effect of the October 1987 crash and concluded that national stock markets became more interrelated after the crash and found that the co movements among national stock markets were stronger when the US stock market is more volatile. Arshanapalli, Doukas and Lang (1995) examined the co integration among stock markets of US, Japan & five other Asian countries and the change in their relationship over a period of time. They also studied the impact of US and Japanese stock markets on five Asian markets for the pre and post 1987. It was found that the extent of co integration among all these markets have increased after the 1987 crash signifying arbitrage activities between US and other Asian markets . Also Asian markets were more integrated with the US market. Janakiramanan and Lamba (1998) conducted a study to find the long term linkages among nine stock markets of pacific basin countries. Daily market stocks of nine countries were taken from 1988 to 1996 countries taken for the study were US, Australia, HongKong, Japan, New Zealand,





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Singapore, Malaysia, Thailand and Indonesia . A positive correlation was discovered except in case of Indonesia which displayed low and statistically insignificant correlation with most markets. Results supported that US market indirectly influence these markets especially in case of New Zealand and Australia. Groenewold et al. (2004) and Li (2007) pointed out the relative isolation of Chinese stock markets from the world markets. Koźluk (2008) concluded that Chinese stock markets are “almost completely separated from global affairs,” but “strongly inter-related” themselves. However, more recently Chow et al. (2011) found evidence of rising integration of the Chinese and world stock markets, measured in terms of comovements of Shanghai and New York Stock exchange prices. The Brazilian stock markets integration can be traced to studies by Yang et al. (2004), Fujii (2005), Khalid and Rajaguru (2007), Rivas et al. (2008), Aktan et al. (2009), etc. However, lack of relationship is found in the studies that utilize cointegrating techniques (Tabak and Lima, 2002; Ozdemir et al., 2009). Agarwal and Du (2005) examined long run inter linkages and short run causality linkages among Indian stock markets and three developed markets namely US, Japan and UK for the post liberalization period of India. Weekly analyses were taken from January 1991 to December 2003. It was found to be integrated in the long run with all the other markets under consideration. It was concluded that in the short run there is unidirectional causality moving from US and Japanese stock markets to the Indian stock markets. Also India is more dependent on these economies for trade and not the other way round. Abas (2009) examined the linkages of the two leading emerging markets, i.e., the Chinese and Indian markets with other developed markets, i.e., the US, the UK, Japan and Hong Kong. They found that the Chinese and Indian markets are correlated with all four developed markets under study. Both markets have at least had unilateral causality with all four developed markets. The empirical results suggested that the benefits of any short-term diversification, or speculative activities, are limited between them. Bhar and Nikolova (2009) explored the level of cointegration of the BRIC with their respective regions and the world in the post-liberalization period, and found that India has the highest level of integration on a regional and world level amongst the BRIC countries followed by Brazil, Russia, and lastly China. The study also suggested the existence of diversification opportunities for China, given its closed nature of the financial system. Bora et al. (2009) examined the emerging market indices of Brazil, Russia, India, China, and Argentina (BRICA) and investigated the linkages among the stock markets of the BRICA countries and their relations with the US market, by employing the VAR techniques to model the interdependencies and Granger causality test to find evidence of a short-run relationship between these markets. The findings showed that the US market had a significant effect on all BRICA countries in the same trading day. Chittedi (2009) examined the integration of the stock markets among the BRIC economies and their integration with the developed countries stock markets such as the US, UK, and Japan using Johansens cointegration, Grangers causality test and Error Correction Mechanism/Model (ECM). It was found that there is cointegration between BRIC countries and developed countries, namely, the USA, UK, and Japan. The results of ECM revealed that the SENSEX, NIKKEI, FTSE and BOVESPA are





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significant. It implies that these markets share the forces of short-run adjustment to long-run equilibrium.

An and Brown (2010) examined the comovements of the weekly and monthly index returns of the US, Brazil, Russia, India, and China stock markets during October 13, 1995-October 13, 2009. Their findings indicated that there is some cointegration between the US and China, while there is no cointegration between the US and the other emerging markets by themselves. Therefore, all of the BRIC stock markets, with the exception of China provide attractive portfolio diversification opportunities for global investors.

Sharma and Bodla (2011) studied the inter linkages among the stock market of India, Pakistan and Sri Lanka from 2003-2010. It was found that while the National Stock Exchange (NSE) influences Karachi Stock Exchange (Pakistan) and Colombo Stock Exchange ( Sri Lanka), the vice versa is not true.

The study of Sheu and Liao (2011) investigated the evolving pattern of integration and Granger causality relationships between the developed US and developing BRIC stock markets. The empirical results demonstrated that the stock markets of Brazil, Russia, and China have begun exerting significant influences on the Dow Jones to some extent after 2006, and the Dow Jones index continues to play a dominant role and increasingly, Granger causing shifts in the emerging markets of Russia, India, and China. The findings pointed out that the time-varying nature of the non-linear cointegration and Granger causality relationships, and also indicated that the potential benefits from international risk diversification may have gradually diminished between these studied markets. Sharma et al. (2013) studied the interlinkages between Brazil, Russia, India, China, and South Africa (BRICS) stock markets with the help of benchmark indices of these stock exchanges. The study revealed that the BRICS stock markets were influenced by each other, but not to a great extent. It implies that there exists opportunities for diversification of the investors among the stock exchanges of BRICS. It was also observed that there are domestic factors (macroeconomic variables) that influence the stock markets. Thus, it is quite clear from the above survey that empirical literature on stock market integration is abundant and results vary according to variable specification, research methodology adopted, participating countries, and period of such study. In this regard, generalization is made complex. Another critical point is that some such studies which analyze a group of countries provide only general conclusions or overall trends rather than results for each country. Also, investigation of the dynamics of the process of integration should reveal the direction of the integration, i.e., whether the markets are becoming more or less integrated during the period of study. This study has looked into the above loopholes in detail. Also, the impact of other BRIC countries on the Indian stock market for the whole study period and its dynamic linkages with them has also been thoroughly examined. OBJECTIVES

The present study is attempted with the following objectives:

1. To analyze the trends in Stock Exchange Indices of BRICS countries.

2. To study the long term relationship between Stock Exchanges of BRICS countries





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3. To observe whether there exist a cause or effect relationship between the Nifty Index and other BRICS indices.

HYPOTHESES The hypotheses of the study are: H01: BRICS Indices are not stationary or got unit root. H02: BRICS Stock Exchange Indices do not affect or cause Nifty Index & vice-versa. H03: There is no co-integration between Nifty Index returns and other BRICS indices returns. RESEARCH METHODOLOGY This research aims to study the linkages between the BRICS countries stock exchanges. This study uses one stock exchange from each of the five countries as a representative of the respective country. The Brail stock exchange is taken as the benchmark of Brazil and the index BM$FBOVESPA is used. For Russia MICEX-RTS Index is taken as the representative exchange. National stock exchange from India is taken as benchmark index and the Nifty General index is used. Shanghai Stock Exchange (SSE) is taken as the benchmark stock exchange for china and Johannesburg stock exchange (JSE) is taken as the benchmark stock exchange for South Africa. The study analyses the daily closing data of the representative Stock indices return for the period 1st June 2011-31st August 2015. Wherever data were missing, the missing values have been filled by taking the average of the two nearest cases. Data have been analyzed using econometric tools. The analysis of econometrics can only be performed on a series of stationary nature. In order to check whether or not the series are stationary, first the Line graph and then the Correlogram is prepared for each of the series. Further to avoid the problem of spurious regressions, it is also necessary to test the order of integration of each index in the model, in order to establish whether it is non-stationary and how many times the index values need to be differenced such that a stationary series can be recovered. The Augmented Dickey –Fuller test and Phillips and Perron (1988) unit-root tests are employed in this study. In order to test for unit roots through ADF tests, we may look at the following equation: ΔYt= α0 + α1 Yt-1 + Σ γj ΔYt-j + εt The ADF unit root test is based on the null hypothesis H0:Yt is not I (0). If the calculated ADF statistics is less than the critical value, then the null hypothesis is rejected; otherwise accepted. If the variable is stationary at level, the variable is said to be integrated of order zero, I (0). If the variable is non-stationary at level, the ADF test can be utilized and the first difference of the variable can be used for testing a unit root. In this case, the variable is said to be co-integrated of order one, I (1) i.e. it has stationary properties. In the second step, The Johansen’s co-integration test has been applied to check whether the long run equilibrium relation exists between the variables. The Johansen approach to co integration test is based on two test statistics, viz., the trace test statistics, and the maximum Eigen value test statistics. The trace test statistics can be specified as: τ trace = -T Σ log (1-λi) Where λ is the ith largest Eigen value of matrix Π, and T is the number of observations. In the trace test, the null hypothesis is that the number of distinct co integrating vector(s) is less than or equal to the number of co integration relations (r).





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The maximum Eigen value test examines the null hypothesis of exactly r co integrating relations against the alternative of r +1cointegrating relations with the test statistics: τ max = -T log (1- λ r+1 ). Where λr+1 is the (r +1) th largest squared Eigen value. In the trace test, the null hypothesis of r = 0 is tested against the alternative of r + 1 co integrating vectors. At the end Granger causality test is employed to find the cause and effect relationships among international stock markets. EMPIRICAL ANALYSIS

The above figure demonstrate the returns on the five stock exchanges. It is indicated from the figure that returns at all the five stock exchanges are stationary in nature.In order to further check the stationarity of the series ,we perform the correlogram and the Unit Root Test in order to further confirm the same.

Now from the unit root tables we find that P value is less than 5% so we reject the null hypothesis and the variable does not have a unit –root, which confirms that the series are stationary at first difference. After confirming that the series are stationary in nature, we proceed to perform the pair wise Granger’s Causality analysis for the five benchmark indices.

7

8

9

10

11

12

250 500 750 1000 1250

LOGSA LOGRUS LOGINDLOGCH LOGBRZ





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Granger’s Causality analysis

Null Hypothesis: F-Statistic Prob.

LOGCH does not Granger Cause LOGIND 2.20765 0.1105

LOGIND does not Granger Cause LOGCH 3.68051 0.0255

LOGBRZ does not Granger Cause LOGIND 2.56015 0.0778

LOGIND does not Granger Cause LOGBRZ 12.5724 4.00E-06

LOGRUS does not Granger Cause LOGIND 0.38355 0.6815

LOGIND does not Granger Cause LOGRUS 5.99479 0.0026

LOGSA does not Granger Cause LOGIND 1.63037 0.1963

LOGIND does not Granger Cause LOGSA 8.09614 0.0003

LOGBRZ does not Granger Cause LOGCH 3.4866 0.031

LOGCH does not Granger Cause LOGBRZ 1.30005 0.2729

LOGRUS does not Granger Cause LOGCH 1.74232 0.1756

LOGCH does not Granger Cause LOGRUS 29.8413 2.00E-13

LOGSA does not Granger Cause LOGCH 1.31093 0.27

LOGCH does not Granger Cause LOGSA 8.62313 0.0002

LOGRUS does not Granger Cause LOGBRZ 46.423 5.00E-20

LOGBRZ does not Granger Cause LOGRUS 281.267 7.00E-99

LOGSA does not Granger Cause LOGBRZ 19.8433 3.00E-09

LOGBRZ does not Granger Cause LOGSA 7.81029 0.0004

LOGSA does not Granger Cause LOGRUS 3.93689 0.0198

LOGRUS does not Granger Cause LOGSA 2.85335 0.0581

Table presents the findings of pair wise Granger’s Causality for the stock exchanges under study. Null hypothesis in the case of Granger’s Causality models that “A” does not granger cause “B”. On those lines table tests the hypotheses about the five stock exchange in pairs. We test the null





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hypothesis at 5%level of significance for which the probability value of less than 0.05implies that the null hypothesis can be rejected while for the probability value of more than 0.05,null hypothesis will be accepted.

The results show that the probability value for the hypothesis LOGIND does not Granger Cause LOGCH, LOGIND does not Granger Cause LOGBRZ, LOGIND does not Granger Cause LOGRUS, LOGIND does not Granger Cause LOGSA is lower than 0.05 which means that in these 4 cases the null hypotheses can be rejected. This implies that NIFTY Granger Causes return at Brazil, Russia, China and South Africa.

In the next step, the co-integration between the two stationary variables has been tested by the Johansson co-integration test.





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Co-integration Test Results

Series: LOGSA LOGRUS LOGIND LOGBRZ LOGCH

Lags interval (in first differences): 1 to 4

Hypothesized Trace 0.05

No. of CE(s)

Eigenv

alue

Statis

tic

Critic

al

Value

Prob

.**

None *

0.0744

96

138.5

6

69.81

889 0

At most 1 *

0.0301

64

55.72

439

47.85

613

0.00

77

At most 2

0.0146

4

22.95

189

29.79

707

0.24

84

At most 3

0.0056

96

7.171

429

15.49

471

0.55

78

At most 4

0.0009

89

1.059

083

3.841

466

0.30

34

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized

Max-

Eigen 0.05

No. of CE(s)

Eigenv

alue

Statis

tic

Critic

al

Value

Prob

.**

None *

0.0744

96

82.83

561

33.87

687 0

At most 1 *

0.0301

64

32.77

251

27.58

434

0.00

98

At most 2

0.0146

4

15.78

046

21.13

162

0.23

79

At most 3

0.0056

96

6.112

346

14.26

46

0.59

88

At most 4

0.0009

89

1.059

083

3.841

466

0.30

34

Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values





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In the above Table we have the result of Johansson Co- integration test which give us the detail whether these two variable have any long run association or not. We have taken the null hypothesis H03: there is no Co-integration between the two variables or there is no long term association between the two variables and alternative hypothesis H13: there is co-integration between the two variables or there is a long term association between the two variables. If P value is more than 5 per cent we accept the null hypothesis and if P value is less than 5 per cent we reject the null hypothesis. Since the P value is more than 5 per cent in all the periods. We will have to accept the null hypothesis that there is no co-integration between the two variable i.e. BRICS Stock Indices and Nifty index return. There is no long run association between these two variables. Thus, the two variables of the study have no long run association or equilibrium relationship between them in full period from 1st June 2011-31st August 2015. CONCLUSION This paper examines the Correlation and Co-integration of the Indian Stock markets with rest of the BRICS countries Stock markets for the period from 1st June 2011-31st August 2015 and results shows there is no long run association between Nifty and rest of the Stock exchanges. A unidirectional causality is observed (NIFTY Granger Causes return at Brazil, Russia. China and South Africa) implying that development in Indian markets are transmitted to the Brazil, Russia. China and South Africa’s Stock markets and further, the Indian Stock market is not caused by the BRICS market. The results and the outcomes can be used to make analysis for better investment and speculative purposes.

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THE INTERDEPENDENCE BETWEEN STOCK …euroasiapub.org/wp-content/uploads/2016/09/11ESSAug-3943-1-2.pdf · examined through cointegration analysis and ... (BSE) and National Stock Exchange