Dynamics of Indian Stock Market Integration with Global stock Markets

 

Dr. Manu K S¹* , Varsha L Menda²

¹Assistant Professor, Department of Management Studies, Christ University, Bangalore.

²Assistant Professor, Department of MBA, Acharya Institute of Technology, Bangalore

 

ABSTRACT:

Financial markets all over the world witnesses growing integration within as well as across boundaries, spurred by deregulation, globalization and technological advancement. This paper investigate the integration and dynamic linkage of Indian Stock market with selected global stock market portraying the scenario to investors to make appropriate investment decisions. It specifically examines the possible volatility for the Indian stock market to ease the process of investors for construction of portfolio investing in global stock markets. The study used daily returns from 1^st April 2004 to 31^st March 2015. Granger causality test is done determining the causality effect. The study found that, the Indian market exhibits a very strong positive correlation with global market. The regression results indicate a significant positive impact of global indices on Indian stock market, thus the Indian market gets affected by the fluctuations in global market. Overall, the Granger Causality test indicates some unidirectional and bi-directional Granger causality effect among the selected markets indicating a dynamic linkage pointing out role on each other’s’ movement. Thus suggesting the foreign institutional investors and Indian investors to accordingly take advantage of positive and negative movements and impart the same for optimum portfolio construction and can induce arbitrage opportunities even in day trading.

 

 

 

INTRODUCTION:

The Indian stock market witnesses spectacular growth, generating interest among foreign as well as domestic growth. Thus leading to examine the market dynamics, particularly index returns volatility. The aim is to identify the volatility mechanism in Indian stock market in the recent years providing insight regarding implications for investors and policy makers. Thus, the study aims in finding the integration and correlation between the Indian stock markets (indices) i.e., NSE and BSE and selected global stock markets. By doing the analysis the possible gains to be reaped out of portfolio diversification from Indian market can be examined and also an indication with respect to vulnerability in country’s stock market can be studied.

 

Some of the benefits of the above topic are higher investment and growth opportunity, risk is reduced, proper allocation of the capital, economic growth of the developing country, reduces the cost of capital and some other benefits are also associated. Liberalization of capital market have led to the development of new financial products and increased the level of portfolio diversification. If the relationship between two markets is less than the investor can invest in international portfolio to earn more profit with less risk associated to it. Due to liberalization of the Indian market by the government the integration of the Indian Stock market have increased through  Foreign Direct Investments (FDI’s), FII’s and by having a trade export and  with the  major of the world’s market. The linkages between the markets will vary as per the crisis that occurs in the markets. In this topic the relationship and the level of integration is examined between the Indian stock Market and 8 global markets such as CNX Nifty 50, Nifty100, Nifty 200, Nifty Midcap 50 and BSE SENSEX with Nikkei 225, Shanghai Composite, NASDAQ, FTSE 100, DAX, KOSPI, CAC 40 and Taiwan Weighted to find the level of impact of changes that occur if there is any changes in the foreign market on the Indian Markets.

 

LITERATURE REVIEW:

The interrelationships, interdependencies, integration and stock markets linkages all over the world are a vastly-researched subject. The same is included as literature providing a brief background of the globe. Rajan Dasgupta (2014) investigated the integrated and dynamic relationship from the period 1^st January 2003 to 31^st December 2012 in short and long run with reference to India with BRIC countries where JB test ,ADF and PP test for judging the normality of data series and found that co-integration exist only in long run relationship. Shegorika Rajwani and Jaydeep Mukherjee (2013) examined the linkage and level of integration between the Indian stock market and other Asian market by applying Unit root test Hansen co-integration technique and found that there exists no integration between Asian market and Indian Stock markets collectively or individually. Srikanth (2012) investigated the link between the Indian stock market and other Asia Pacific region by using ADF Unit root test and found that there exist stationarity and Johansen and Juselis co-integration suggests that there exist integration between Indian stock market and other Asian Pacific region markets for the collected monthly data from January 2000 to December 2010. Sarkar, Chakrabathi and Sen (2009) studied the volatility of the Global market and the Indian stock market and found that there is a strong proof of global contagion and there exist some volatility among the global market and the Indian stock market. Saji (2014) examined the long and short run relationship of major developed markets with Indian market of the world during the year 2005 to 2013 to analyze whether financial recession  offer better diversification to Indian investors in global market where Johansen’s co-integration method failed to prove the evidence of integration in stock price after the recession. Taneja (2012) studied the short and long run relationship between Indian stock exchange and major world financial markets for the period 1999-2010 by using ADF test, PP test and found that is significant existence in the long run association of Indian stock market with major countries market such as U.S , France , Japan. Sarat dhal(2009) studied whether the integration of financial markets are associated with the global market crisis by using multivariate co-integration analysis of stock price indices and concluded that global crisis doesn’t affect the market in the long run relationship break down. Zeren and Koc (2013) studied the long run relationship between the turkey and G8 countries of the world by using Kapetanios Unit root test and Maki co-integration test to analyze the break and structural break for the time period from November 1990 to December 2012 and concluded that US, UK, Japan and France market are co-integrated in long term and that of Canada and Germany are not integrated. Ho and Odhiambo (2012) investigated the relationship that exist between economic growth and stock market development using time series data of the time period for the year 1980 to 2010 to analyze the long run co-integration relationship by following Autoregressive Distributed lag (ARDL) and Granger Non Causality test fails to yield a long run casual relationship between economic growth and stock traded value and only short run is detected from the economic growth and stock traded value. Menon, Subha and Sagaran (2009) studied whether the Indian stock market have link with other leading stock markets like America, china, etc., by using Engle Granger test for co-integration for the time span of 10 years i.e., from April 1997 to May 2007 using daily index values showed that there is absence of co-integration and interdependency between the Indian stock market and US as well as other major stock markets. Chittedi (2010) studied the stock market integration between India and developed countries such as US, UK, Japan etc., for the period of 10 years from 1^st October 1997 to 1^st October 2007 using daily stock market closing indices by conducting Unit root test, Granger causality and Error correction mechanism and found that there exist Co integration between Developed countries stock and Indian stock market. Dasgupta(2014) examined the linkage between Indian stock market and 4 SAARC countries indices in the long and short run by using monthly indices data for the period from April 2007 to March 2012 using ADF ,PP ,Johansen and Juselius Co integration test, Granger causality test and concluded that there exist Co integration and long run relationship between Indian stock market and other countries and short run unilateral and bilateral relationship between the stock markets of SAARC and the Indian market. Kumar, Raju and Shahab (2012) studied the interaction between changes in the exchange value of Indian rupee for Dollar and Euro, by using daily data of the 10 years for the period January 2000 to July 2010 considering Adler Dumas Model (Regression Model), and found out that there is stationarity among the currencies. Sharma and Bodla (2011) examined the interlinkage between India, Pakistan and Srilanka Stock market using Unit root test, Grangers causality model and Vector Auto regression (VAR) model for the period of January 2003 to June 2010 considering closing prices of the indices and concluded that there exist stationarity among the markets and the investors can diversify the investment. Arshanapalli and Kulkarni (2001) examined the linkage and the extent of nature of relationship between Indian stock market (BSE) and New York Stock Market (NASDAQ) for the period of 9 years i.e., from January 1991 to December 1999 using the Co integration and Granger causality and concluded that Indian stock market is not interrelated with US stock market for the considered sample period of time. Raj and Dhal (2008) investigated the financial integration and correlation of Indian stock market with major regional and global markets for the period of 15 years by using the daily closing prices by conducting the Eigen value tests of Johansen’s VECM and concluded that there is more integration between Indian stock market and global and major regional markets. Hamid and Hasan (2011) studied the dynamic linkage between the Karachi stock exchange with the emerging stock markets like India, China and with developed stock markets like USA, Japan for the time period of 10 years from January 1998 to December 2008 with the help of correlation analysis, Co integration test, unit root test, granger causality test and found that there exist a short run and long run relationship between the stock markets. Srikanth and Aparna (2003) examined the degree of stock market integration with monthly average data for BSE, NASDAQ, NYSE, S and P 500 and others by using correlation t-test and concluded that their exist a linkage between domestic and international markets. Subha and Nambi (2010) studied the interdependency of the Indian stock market with the American stock market for a time period from 1^st January 2000 to 31^st December 2008 by using the Engle Grangler test of Co integration and concluded that there is no existence of an interlink age between the NASDAQ and BSE by using the daily closing price of stocks. Mukerjee (2007) compares the relationship between the Indian stock market with  International markets from 1^st January 1995 to 31^st July 2006 between NSE, NYSE, and KSE by using statistical analysis and efficiency test and found that there is an impact of one stock market with the other. Wong, Agarwal and Du (2005) investigated the short run dynamic linkage between the Indian market and major developed markets for 13 years by using unit root test, VAR Model, Co integration error and concluded that there exists integration between the markets. Shachmurove (2006) explored the dynamic interrelationship between Indian and other Brazil, US, China and Russia by applying VAR model for the period of 10 years and concluded that some markets affect directly and some other markets affect less. Sheu and Liao (2011) investigated the relationship between US and BRIC countries by using Granger causality and demonstrated that when there are changes in the US market affects the Brazil, Russia, China and India and has mentioned that the integration depends on the time varying nature. Tripathi and Shruti (2010) studied the integration between the Indian and US, UK, Japan and China from 1^st January 1998 to 31^st October 2008 with the help of Granger’s causality test and Engle-Granger Co integration test and concluded that except US market there sexist no relationship between the other markets. Bala and Mukund (2001) studied the extent and nature of relationship between the US stock market and the Indian stock market by using Co integration for aneroid of 9 years from January 1^st 1991 to December 31^st 1999 and found that Indian stock market is not affected by the movements in the US stock market. Chan, Benton and Min (1997) studied the stock market integration between 18 countries by using the Johansen’s Co integration test method covering a period of 32 years by analyzing the markets collectively as well as individually and found that the evidence of Co integration among the stock market is very less in number when compared to other markets. Poshakwale (2002) tested the non linear dependence by using the closing prices of 38 stocks from BSE and conformed that there is a nonlinear significance among the markets. Pan, Fok and Lui (1999) studied the stock price and exchange rates casual relationship using daily closing price by Granger test and found that there exist a strong relationship after the Asian Crisis. Olowe (2009) examined the Nigerian stock return and it volatility to the global financial crisis using GARCH model and concluded that there is no volatility from the crisis. Ali and Afzal (2012) investigated the impact of global crisis on the India and Pakistan markets for the period from 1^st January 2003 to 31^st August 2010 by using EGARCH model and found that there is more of negative impact than of positive effect.

 

MATERIAL AND METHODS:

Objectives of Study:

The primary objective of this study is to analyse the impact of global markets on selected indices of NSE and Sensex and examine the causality effect between Indian stock indices and Global stock market indices. Further, to evaluate the interrelationship and integration between Indian Stock markets and other selected global stock markets validating the dynamic linkages between these markets.

 

Hypothesis of the Study:

The hypothesis is framed on the basis to determine if there exists any causality effect i.e. determining the short and long-run interrelationships and integration of the stock markets and to determine the stationarity i.e. whether the data series of the indices contain any unit-root or not.

(H₀)a : There is no significant impact of selected global stock markets indices (Nikkei 225, Shanghai Composite, NASDAQ, FTSE 100, DAX, KOSPI, CAC 40 and Taiwan Weighted) on CNX Nifty 50.

 

(H₀)b: There is no significant impact of selected global stock markets indices (Nikkei 225, Shanghai Composite, NASDAQ, FTSE 100, DAX, KOSPI, CAC 40 and Taiwan Weighted) on CNX Nifty 100.

 

(H₀)c: There is no significant impact of selected global stock markets indices (Nikkei 225, Shanghai Composite, NASDAQ, FTSE 100, DAX, KOSPI, CAC 40 and Taiwan Weighted) on CNX Nifty 200.

 

(H₀)d: There is no significant impact of selected global stock markets indices (Nikkei 225, Shanghai Composite, NASDAQ, FTSE 100, DAX, KOSPI, CAC 40 and Taiwan Weighted)indices on Nifty Midcap 50.

 

(H₀)e:There is no significant impact of selected global stock markets indices (Nikkei 225, Shanghai Composite, NASDAQ, FTSE 100, DAX, KOSPI, CAC 40 and Taiwan Weighted) on BSE SENSEX

 

(H₀)f: The presence of unit root in the daily return series of selected Indian stock market indices and selected global stock market indices or the return series of the selected indices are non-stationary.

 

(H₀)g: The daily return series of selected Indian stock market indices do not granger cause the daily return series of selected global stock market indices.

 

Data and Methodology:

The data employed in this study are daily returns computed from daily closing price of stock market indices for India (Nifty 50, Nifty 100, Nifty 200, Nifty Midcap 50 and BSE SENSEX), Japan (Nikkei 225), China (Shanghai Composite, Taiwan Weighted), U.S (NASDAQ), UK (FTSE 100), Germany (DAX), South Korea (KOSPI), and French (CAC 40) from April 1, 2004 to March 31, 2015. The indices prices are not converted into any common currency, as the problems associated with transformation, due to fluctuations in exchange rates. Further the return series for each index has been computed using the formula as follows:

Where,

P1=today’s closing price of the respective index, P0=yesterday’s closing price of the respective index, ln=natural logarithm.

From the return calculated descriptive statistics is computed to determine the data series normality. Further regression test is carried to examine the impact of global indices on the Indian stock market. The important criteria to examine the computed data series is having a causality effect among each other Granger Causality test is used and to find out the stationary of data series ADF test is used. To explain the Granger test, let’s consider stock price and open interest relationship: Is it X (Stock price) that “causes” the Y (Open Interest) (X→ Y) or is it the Open interest (Y) that causes X (Y→X), where the arrow points to the direction of causality. The Granger causality test assumes that the information relevant to the prediction of the respective variables, X and Y is contained solely in the time series data on these variables. The test involves estimating the following pair of regressions:

 

                (1)

 

                (2)

 

Where, it is assumed that the disturbances e_1t and e_2t are uncorrelated. Further, because we have two variables, we are dealing with bilateral causality. Equation (2) explains that current X (Stock price) is related to past values of itself as well as that of Y, and (1) explains  a similar behavior for X. Note that these regressions can be cast in growth forms, ˙X and ˙Y, where a dot over a variable indicates its growth rate. We now distinguish four cases:

(i) Unidirectional (one way) causality from Y to X

(ii) Unidirectional (one way) causality from X to Y

(iii) Bilateral causality, is suggested when the sets of Y and X coefficients are statistically significantly different from zero in both regressions equations.

(iv) Finally, independencies suggested when the sets of Y and X coefficients are not statistically significant in both the regressions.

 

EMPIRICAL RESULTS AND INTERPRETATION:

Table-1(A): Descriptive Statistics of Indian Stock Market Indices

	Minimum	Maximum	Mean	S. D	Skewness	Kurtosis
NIFTY_50	-15.2303	16.3343	0.0675	1.7570	-0.3906	10.7069
NIFTY_100	-15.9565	15.9417	0.0687	1.7505	-0.5553	10.9132
NIFTY_200	-17.7480	15.4829	0.0665	1.7293	-0.7529	12.5636
NIFTY_MIDCAP50	-22.1242	13.0969	0.0557	2.0625	-0.8584	10.6012
BSE_SENSEX	-12.7960	15.9900	0.0694	1.7145	-0.2285	9.0039

(Source: Researcher’s own calculation)

 

The Table-1(A) represents the statistics summary of the Indian Stock Market indices under this study. The average mean value of BSE SENSEX is higher than that of other indices. The value of standard deviation of NIFTY MID CAP50 depicts highest volatility in data. The value of skewness for the above variables indicates lower values during the study period which indicates volatility and deviation from normal distribution in data series. Thus the distribution is negatively skewed. The kurtosis indicating leptokurtic distribution where the values are more concentrated about the mean having thicker tails.

 

Table-1(B): Descriptive Statistics of Global Indices

(Source: Researcher’s own calculation)

 

The Table-1(B) represents the statistics summary of the Global indices under this study. The average mean value of DAX is higher than that of other indices. The value of standard deviation of Shanghai depicts highest volatility in data. The value of skewness for KOSPI, NASDAQ, NIKKEI 225, and Shanghai indicates lower values during the study period which indicates volatility and deviation from normal distribution in data series. Thus the distribution is negatively skewed whereas CAC_40, DAX, FTSE_100, TAIWAN WEIGHTED are positive skewed. The kurtosis indicating leptokurtic distribution where the values are more concentrated about the mean having thicker tails.

 

Table-2: Representing Cross-Correlation Results

(Source: Researcher’s own calculation)

 

Table-2 Continue

 

The Table-2 presents the Cross-correlation for the various indices which acts as a preliminary indicator for the equity integration; indicating positive correlation with the level of significance for the analysis period considered under the study. It has pointed out that there exists high level of significance among the indices chosen for the study as all the stock markets are positively correlated to each other. Among, these NSE_NIFTY_50, CNX_100 followed by Sensex are highly correlated to KOSPI.

 

Table-3: Representing Regression Results

Note:  *** Significant at 1% level  ** Significant at 5% level  (Source: Researcher’s own calculation)

 

Table-3 Continue

 

The consolidated regression results found derives the following regression equations for each Indian market indices and global indices.

 

                                                                             

Table 3 shows the regression results. The R² value of all the indices shows that 33.90% of the Nifty 50, 33.91% of Nifty 100, 33.51% of Nifty 200, 7.02% of Nifty Midcap 50  and 32.85% BSE SENSEX fluctuations could be explained by all the selected Global market indices. Remaining variations of respective indices will be explained by other factors. The F value which is significant in all the cases, which indicates that the regression model explains major proportion of variations in performance of selected Indian stock market indices, which shows there is a strong impact of global stock markets on Indian stock market. Further, the regression results found that the coefficients of Nikkei 225, Shanghai Composite, NASDAQ, DAX, KOSPI and Taiwan Weighted indices are positively significant except CAC_40 at 1% level.  It indicates that all these global stock markets have strong positive impact on Indian stock market. Thus, whenever the global index value increases the selected Indian stock indices of NSE and BSE also increases, additionally whenever the global index value decreases the selected Indian stock indices of NSE and BSE also decreases. Thus, whenever there is increase in global index value the selected Indian stock indices of NSE and BSE decreases and the vice versa. The study uses ADF test to determine whether the data series with respect to indices contain unit-root or not and find out the stationarity.

 

Table 4: shows the Augmented Dickey Fuller ( ADF) test results

Level - Critical value at 1% significance level is –3.4359***, at 5% significance level is –2.8632** and at 10% significance level is –2.5677*(Source: Researcher’s own calculation)

 

The Augmented Dickey Fuller (ADF) test results in Table-4 clearly show that there is no presence of a unit-root in the levels of all selected Indian and global indices. There is no evidence to support the presence of a unit-root in selected stock market indices. Hence, changes in stock returns are stationary. In other words, all stock market indices series are stationary at level. The study uses Granger causality tests results to find out any possible causal relationships in between selected Indian stock market indices and global stock market indices in the short-run. The hypothesis which exhibits the series is non-stationary is rejected under ADF results. Hence it can be inferred that the Indian stock market integrates with the global stock markets exhibiting a long-run relationship with each other. Thus there exist a long-term integration between Indian stock market and global stock market. Further the study uses Granger Causality test results to validate dynamic linkages and integration, further determining if any casual relationships exits among the indices.

 

Table-5A: Shows Granger Causality Test results

Note:  *** Significant at the 1 % level. (Source: Researcher’s own calculation)

 

Table-5B: Showing number of combination of indices which are Uni and bi directional Granger causality effect

(Source: Researcher’s own calculation)

 

Results from the granger causality test for the selected Indian stock market indices and global market indices are exhibit in Table-5. It has shown bi-directional Granger causality effect between Indian stock market indices and Global stock market indices. The summarized table clearly has shown bi-directional Granger causality effect between Sensex and Shanghai, Sensex and KOSPI, Sensex and FTSE100, Sensex and DAX, Sensex and CAC40, Nifty_50 and Shanghai, Nifty_50 and KOSPI, Nifty_50 and CAC40, CNX100 and Shanghai, CNX100 and KOSPI, CNX200 and Shanghai, CNX200 and KOSPI. This proves that cause and effect relationship is bidirectional and runs two way. The results have shown global stock market indices granger causes unidirectional the Indian stock market indices. Among the selected global stock market indices, NASDAQ granger causes unidirectionally all the selected Indian stock market indices indicating one way cause and effect i.e. there is a significant effect from NASDAQ on all the selected Indian stock market indices, followed by DAX on CNX100, CNX200, Nifty50; CAC40 and FTSE100 on CNX100, CNX200, Midcap; Taiwan weighted and Shanghai on CNX Midcap. Further, the results indicate that the Indian stock market indices are unidirectional granger only to TAIWAN_WEIGHTED stock index. Overall, the test indicates some unidirectional and bi-directional Granger causality effect among the selected markets. This proves that the daily return series of selected Indian stock market indices do have granger cause with daily return series of selected global stock market indices.

 

CONCLUSION:

The Indian market exhibits a very strong positive correlation with global market. The regression result indicates a significant positive impact of global indices on Indian stock market indicating the Indian market gets affected by the fluctuations in global market. Overall, the Granger Causality test indicates some unidirectional and bi-directional Granger causality effect among the selected markets indicating a dynamic linkage pointing out role on each other’s’ movement. More interestingly the study found that the Indian stock indices have uni-directional Granger causality effect only on Taiwan_Weighted index. Further, NASDAQ has uni-directional Granger causality effect on most of the Indian stock indices compared to other global market indices, Thus there exist a penetration of global market in the Indian stock market and exhibiting a relationship that these investment activities play a crucial role in economic growth. Therefore, the above results support the hypothesis framework used in the study. In this financially interconnected world, the study helps the stakeholders understanding the relationships between the economies resulting to the equity-market investors to frame effective diversification strategies and adjusting their portfolios accordingly to achieve the gain. Further the research study carried indicates a preferable destination for investment among the countries.

 

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