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Vol. 6, No. 1, January-June (2011) : 49-65



T. Ramesh Babu* and M. Venkateshwarlu*

Abstract: An important objective of reforms in India has been to integrate the various segments of the

financial market for bringing about a transformation in the structure of markets, reducing arbitrage opportunities, achieving higher level of efficiency in market operation of intermediaries and increasing efficacy of monetary policy in the economy. In the copious literature, however, studies focused on India’s stock market are rather scarce, despite various stylized facts suggested, prima facie, the growing linkage of the Indian market with global and major regional markets. The degree of cointegration of the stock markets is an important factor in deciding the potential benefits of portfolio diversification. The primary focus of this paper is to empirically explore the dynamic integration of India’s stock market (S&P CNX NIFTY) with the global stock markets like Japan, Hong Kong, Germany, Spain, UK and USA. The novel work of the present research is to estimate the long run relationship by a two step estimation methodology using cointegration analysis and Artificial Neural Networks. This paper also analyses the short-term integration between global markets, before the recent recession of 2007 and during the recession period; this will help investors to understand the market co-movements from equilibrium to non-equilibrium space. The findings of this study may also help the regulators in monitoring and achieving price stability in financial markets.

Keywords: Emerging markets, Global financial markets, Market integration, Portfolio diversification, G10, G11, G15

1. INTRODUCTION National stock markets have emerged as the major channel for financial integration of emerging market economies amid globalisation, deregulation and advances in information technology. An important objective of reforms in India has been to integrate the various segments of the financial market for bringing about a transformation in the structure of markets, reducing arbitrage opportunities, achieving higher level of efficiency in market operation of intermediaries and increasing efficacy of monetary policy in the economy (Reddy, 2003, 2005) and (Mohan, 2004, 2007). In financially integrated markets, capital should flow across borders in order to insure that the price of risk (i.e., the compensation investors receive for bearing risk) is equalized across assets. Conversely, if capital controls or other forces prevent free movement of capital across borders, then it is likely that different economies will demand different levels of compensation for risk (Robert, 1995).

The degree of linkages or integration among the stock markets provides important implications for the potential benefits of the international portfolio diversification and financial stability of a country (Ibrahim, 2005).

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Among the factors contributing to growing financial integration are rapid increases in the cross-border mobility of private capital inflows due to investors seeking portfolio diversification and better yields, a growing reliance of nations on the savings of other nations, and a shift in the leverage preference of companies from debt to equity finance and vice versa. It is generally perceived that financial integration can be associated with several benefits, including development of markets and institutions and effective price discovery, leading to higher savings, investment and economic progress. At the same time, linkages among financial markets can pose various risks, such as the contagion and associated disruption of economic activities that were evident during the crisis in Asia in the late 1990s. More recently, in January 2008, national stock markets declined sharply in the wake of credit market developments in the United States. Economists have thus realized that it is useful for countries to monitor the progress of interdependence among financial markets for the sake of policy as well as market participants (Janak and Sarat, 2009).

Before 1970s, empirical studies on market integration reported lower correlations among national stock markets (Solnik, 1974), implying the existence of potential benefits of international portfolio diversification. Nowadays, however, the world capital markets have been increasingly integrated and co-movements among the leading world financial markets have been rising (Blackman et al., 1994; Masih and Masih, 1997; Ghosh et al., 1999). Based on a survey on the available empirical evidence on market integration across national capital markets, Goldstein and Michael (1993) found that the international links have been increasing over the past decade, especially for the stocks traded actively in the major financial centers. The study also found that the emerging markets are becoming more closely integrated with markets in the rest of the world, although their integration progress has been far less than that of the industrialized countries. This implies that the potentialities of portfolio diversification benefits across the world stock markets in the long run have been diminished. In addition, an increasing integration among the national stock markets further implies that international financial instabilities are easily transmitted to domestic financial markets, a phenomenon called “financial contagion” (Ibrahim, 2005).

There has been vast research work carried on market integration among developed countries, contrary to that there have been relatively few studies exploring the issue of stock market integration in the emerging markets especially Asian markets. There has been some amount of research carried on the market integration of Association of Southeast Asian Nations (ASEAN) stock markets. For example, Roll (1995) affirmed that although Indonesia has an active equity market for a number of years, no empirical studies on this market have appeared in the western scholarly journals. Recently, Majid et al. (2009) investigated the dynamic linkages among ASEAN-5 emerging stock markets.

In developed economies, such as the USA, Japan and Germany, both market integration and segmentation are well documented. Recognizing the critical importance of financial assets to economic agents and policy, numerous studies in the applied finance Dynamic Integration of an Emerging Stock Market with World Stock Markets 51 literature have concentrated on measuring the international integration of national stock markets across several developed and emerging market economies. In the copious literature, however, studies focused on India’s stock market are rather scarce (Ahlgren, 2002), despite various stylized facts suggested, prima facie, the growing linkage of the Indian market with global and major regional markets in Asia during the reform period beginning in the early 1990s (Arshanapalli, 1993).

Surbhi and Bhanumurthy (2005) has studied the financial market integration in India during the post-1991 period by using monthly data on call money rates, 91 day Treasury Bill rates, Indian Rupee/US dollar exchange rates, and the London Inter Bank Offered Rate (LIBOR). By using a multiple co-integration approach, the study found that there is a strong integration of the domestic call money market with the LIBOR. Though, the study found that there is a long-term co-movement between domestic foreign exchange market and LIBOR, it is not robust. Hansda and Ray (2002) have studied the relationship between BSE and NASDAQ with respect to Globalisation, Information Technology and Stock Prices. Wong (2005) has examined the financial integration for Indian stock market with a fractional cointegration approach. However, in recent years, the vast-growing economic activities and the increasing investment opportunities in Indian emerging markets have attracted investors and researchers attention.


2.1. Data To provide more robust and updated results, this study uses daily closing data of the stock market indices. The data considered for the present analysis has been divided into two segments; first period is the pre-recession period from 1 January 1998 to 31 December 2006 and the second period is the recession period from 1 January 2007 to 31 March 2009.

The main reason of the above division is to understand the short-term market comovements from equilibrium to non-equilibrium space. All these indices are denominated in local currency units; the data has been extracted from the following sources (Centre for Monitoring Indian Economy, National Stock Exchange of India and World Federation of Exchanges). The stock returns are calculated from the indices defined in Table 1.

2.2. Methodology The primary focus of the present research is to exclusively study the integration of India’s stock market, (National Stock Exchange of India - S&P CNX NIFTY index) with global stock markets. The findings of this study may have implications for investors and companies who want to diversify their investments internationally and make capital

budgeting decisions in this region. The objectives of this study are therefore to:

– Examine empirically the short term co-movement of global markets before the recent recession of 2007 and during the recession period.

52 International Economics and Finance Journal

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– Examine the dynamic causal linkages among the global stock markets.

– Explore the market integration of S&P CNX NIFTY in terms of long-run equilibrium with respect to global markets.

The present work has been organized as follows, starting with the mathematical modeling framework, then a brief analysis on the descriptive statistics, short term integration with cross correletions, unit root tests, causal linkages and a two step estimation methodology to investigate the long-term integration using cointegration analysis and Artificial Neural Networks (ANN).


Since the main purpose of the present work is to investigate the stock market integration in terms of the long-run equilibrium relationships, for this purpose a two-step estimation methodology using cointegration analysis and Artificial Neural Networks (ANN) have been adopted. The reason for this is that, cointegration analysis is able to detect whether the integrated markets exist or not in the sense that there is a tendency for a long-run equilibrium relationship among the markets to move together in the long run, while allowing for deviations from the short-run equilibrium. On the other hand, the ANN estimation gives us the cross checking ability of long term relationship established by the cointegration analysis. The two step method adopted in the present study has a great advantage because; the combined model becomes hybrid. The term hybrid is used in a sense that, cointegration analysis is a linear and parametric while ANN is a non-linear and semi-parametric methodology. The accuracy of the results obtained from the above analysis depends on their consistent prediction by both of the methods.

3.1. Cointegration Analysis To test for cointegration among the national stock markets, the ML approach of Johansen (1988) and Johansen and Juselius (1990) are very famous, henceforth the JJ cointegration Dynamic Integration of an Emerging Stock Market with World Stock Markets 53 approach is adopted. Essentially, the JJ test started its cointegration model with a Vector

Autoregressive [VAR(k)] model as follows:

Yt = A1Yt-1 + A2 Yt-2 + …. + AkYt-k + εt (1)

Where, Yt = (Y1t; Y2t;... ; Ynt)’. Subtracting Yt-1 from both sides of equation (1) to have:

∆ Yt = (A1 – I) Yt-1 + A2Yt-2 + …+ AkYt-k + εt (2)

Then adding and subtracting (A1 – I) Yt-2 from both sides to get:

∆ Yt = (A1 – I) Yt-1 + (A2 + A1 – I)Yt-2 + …+ AkYt-k + εt (3) Repeating addition and subtraction in this fashion, following the studies of Kasa (1992) and Heinesen (1995), the JJ cointegration model can therefore, be formulated as


∆ Yt = δ + Γi ∆Yt-1 + ……… + Γk∆Yt-k + Π Yt-k + εt (4) Where Yt is an n×1 vector of variables and δ is an n×1 vector of constants, respectively.

Γ is an n×n matrix (coefficients of the short-run dynamics), Π = αβ´, Where α is an n×1 column vector (the matrix of loadings) represents the speed of short-run adjustment to disequilibrium and β´ is an 1×n cointegrating row vector (the matrix of cointegrating vectors) indicates the matrix of long-run coefficients such that Yt converge in their longrun equilibrium. Finally, εt is an n×1 vector of white noise error term and k is the order of autoregression. As our study investigates market integration among the 9 global markets, n is equal to nine.

The above process from VAR model equations (1) and (4) is called the cointegrating transformation. The long-run information matrix Π in this equation is the key to Johansen’s cointegration test because its rank r determines the number of cointegrating vectors.

If rank (Π) = 0, equation (4) returns to a VAR(k) model in the first differences and the components in Yt are not cointegrated. On the other hand, if Π is a full rank n, all component in Yt are stationary. In a more general case when 1 rank (Π) n, the number of cointegrating vectors is equal to r, the rank of matrix Π. Since the rank of a matrix is equal to the number of Eigenvalues λi (or characteristic unit roots) that are significantly different from zero, Johansen proposed two statistics to test the rank of the long-run

information Π, namely:

λtrace(r) = –T Σ ln(1 – λi) (5) λmax(r, r + 1) = –T ln(1 – λr+1) (6) Where λi are estimated Eigenvalues (characteristic roots) ranked from largest to smallest. The λtrace in the equation (5) is called the Trace Statistic (TS), which is a likelihood ratio test statistics for the hypotheses that are at most r cointegrating vectors.

The λmax in the equation (6) is called the Maximal Eigenvalue Statistic (MES) that tests the hypothesis of r cointegrating vectors against the hypothesis of r-1 cointegrating vectors.

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