Market efficiency and international diversification: Evidence from India

International Review of Economics and Finance 19 (2010) 313–339

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International Review of Economics and Finance

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Market efficiency and international diversification: Evidence from India

Mehmet F. Dicle a,⁎, Aydin Beyhan b, Lee J. Yao a

a J A Butt College of Business, Loyola University New Orleans, United Statesb Faculty of Trade, T.C. Yeditepe University, Turkey

a r t i c l e i n f o

⁎ Corresponding author. 6363 St. Charles Avenue, BE-mail address: [email protected] (M.F. Dicle).

1 GDP and FDI statistics are provided by; UNCTAD,http://www.unctad.org/sections/dite_dir/docs/wir08_

2 Please refer to Misra (1997) for the history of Ind

1059-0560/$ – see front matter © 2009 Elsevier Inc.doi:10.1016/j.iref.2009.09.003

a b s t r a c t

Article history:Received 24 November 2008Received in revised form 30 April 2009Accepted 1 June 2009Available online 1 October 2009

This study evaluates one of themost important emergingmarkets, India (Bombay Stock Exchangeand Indian National Exchange), for its efficiency and for its potential to offer diversificationbenefits to international investors. Market-wide tests include; 1) contemporaneous relationship,2)Granger type causality and 3) day-of-the-week effect. Tests on individual Indian stocks include:1) panel estimation of Granger causality, 2) stock-by-stock estimation of Granger causality and3) runs test. In sum, Indian markets are well integrated with the international equity markets, acharacteristic that lowers the international diversification benefits. While day-of-the-week effectis an international spillover, itmay be possible to predict individual Indian stocks' returns throughcausality with international equity markets and through momentum trading techniques.

© 2009 Elsevier Inc. All rights reserved.

JEL classification:G14G15

Keywords:Day-of-the-week effectMarket efficiencyBombay Stock ExchangeIndian National ExchangeIndia

1. Introduction

After the October 1987 crash, academic attention to international integration of financial markets is significantly heightened.Main issues are the contagion of financial crises and portfolio diversification across markets. Grubel (1968) shows the importanceof international diversification that requires the existence of independent financial markets. Such independence becomes vital forthe development of emergingmarkets to attract international investors. Any relationship (dependence, correlation, co-integrationor predictability) to developed markets (or to each other) would diminish their appeal for international diversification.

This study evaluates an important emerging market, India, for its efficiency and potential to offer diversification benefits tointernational investors. India is chosen for several reasons. India's economy has grown significantly from about $330billion GDP in1990 to $1.1trillion GDP in 20071. India has also taken steps2 to increase its international integration including the establishmentof Securities and Exchange Board of India (SEBI) in 1988 and increasing ownership level of Foreign Indirect Investment (FII) to 49%in 2001. In terms of foreign direct investment (FDI) stock in India, 1990 level of $1.7billion is increased to about $76billion in20072. While the increase in stock of FDI in India is significant, the increase in Indian international direct investment is equallysignificant. In 1990, the level of international investment was $124million and this level is increased to about $29billion in 2007. Itis important to note the important change in India's international integration both in terms of FDI towards India and FDI fromIndia.

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World Investment Report 2008; www.unctad.org/wir or www.unctad.org/fdistatistics available throughfs_in_en.pdf.ian markets.

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314 M.F. Dicle et al. / International Review of Economics and Finance 19 (2010) 313–339

Foreign trade data for India, as provided in Table 1, also shows the level of international trade integration achieved by India. In1990, the total foreign trade was about $44billion3. This level is increased to about $271billion in 2006. With its biggest foreigntrade partner, United States, the level is increased from $6billion in 1990 to $34billion in 2006. The trade relationships withChina, United Arab Emirates and Singapore are also improved significantly. Most importantly, the Indian economy became moreinternationally integrated in 2006 than it was in 1990.

As India becomes more internationally integrated, it provides an opportunity for an important case study. With higherinternational integration through FDI and international trade, do markets in India become more integrated? Based on theargument about emergingmarkets offering diversification benefits to international investors, are Indianmarkets still independentequity markets? While there is academic interest to evaluate India for its potential for international diversification and for itsefficiency, the results are mixed in the literature. There is evidence to support international independence and efficiency of Indianmarkets (i.e. Sharma & Kennedy, 1977), however, there is also evidence to support international dependence (among others;Kumar &Mukhopadyay, 2002;Wong, Agarwal, & Du, 2004; Bahng, 2005; Hassink, Bollen, & De Vries, 2008 and Sarkar, Chakrabarti,& Sen, 2008) and weak relationship (Nath & Verma, 2003). This study aims to provide robust evidence to prove whether Indianmarkets are independent and efficient, thus offer diversification benefits to international investors.

Two of the markets in India are analyzed individually: Bombay Stock Exchange (BSE) and Indian National Exchange (INE) fortheir relationship with the international equity markets and for their efficiency. International equity markets include 49 equityexchanges from 32 different countries. The data for 35,003 stocks traded on international markets and 1522 stocks traded onIndian equity markets are provided by Dicle (2008) based on Reuters4 for the period between January, 2000 and December, 2007.Market independence and efficiency are evaluated through several tests at the market level and at the individual stock level.

First, the contemporaneous relationship is evaluated between Indian markets' returns with international equity markets'returns. In order to account for serial correlation and heteroskedasticity in the residuals, generalized autoregressive conditionalheteroskedasticity (GARCH) model is employed for market-wide estimations. The results for these tests are robust to controlvariables such as contemporaneous market liquidity, lagged returns for international and Indian markets. Second test extends theinitial evidence to evaluate whether such contemporaneous integration may be to blame for day-of-the-week effect reported inprevious literature (Poshakwale, 1996; Choudhury, 2000 and Bhattacharya, Sarkar, & Mukhopadhyay, 2003) for Indianmarkets. Interms of market efficiency, any predictability and anomaly of returns is important to evaluate for possible explanations. Jaffe andWesterfield (1985) argue that day-of-the-week effect may be related to integration with the U.S. equity markets. Accordingly,integrationwith International equitymarkets is evaluated as a possible reason for day-of-the-week effect in Indianmarket returns.

While different methodologies are used in previous literature to evaluate Indian markets' independence, Granger causalitymodels find supporting evidence for causality from international markets to Indianmarkets (Kumar &Mukhopadyay, 2002;Wonget al., 2004; Bahng, 2005 and Sarkar et al., 2008). Based on the evidence obtained with the initial tests, the significance of laggedinternational market returns provide the basis for the Granger causality from international markets to Indian markets as the thirdtest of the study. Contemporaneous relationship and Granger causality analyses test international integration and causality,therefore international independence, for both of the Indian markets. In terms of market efficiency however, such internationalcausality may be used to predict returns for individual Indian stocks. In order to test this possibility, a fourth test of the studyevaluates individual Indian stocks for their individual causality with international markets' returns. Indian market returns, stocks'own liquidities and difference in their own squared returns (to proxy for variance in returns) are controlled within a Granger typecausality model (using VAR estimation).While overall marketsmay ormay not offer diversification benefits, theremay be numberof stocks in Indian markets that may offer international diversification benefits (those stocks that are not caused by internationalmarkets). Grinblatt, Titman andWermers (1995), Nofsinger and Sias (1999) and Badrinath andWahal (2002) show the preferenceof mutual funds for past winners. Gompers and Metrick (1999) argue for the institutional investors' preference for large stocksand, in addition, Ferreira andMatos (2008) argue for the preference for the higher liquidity. Therefore, international (institutional)investors may be causing Indian markets through some specific stocks with certain attributes.

The runs test is performed as the final test for this study to evaluate efficiency of the Indian markets and predictability ofindividual Indian stocks similar to Sharma and Kennedy (1977). The evidence provided with the runs test points to furtherdifferences between BSE and INE. The level of non-random returns may point to possible predictability for some of the Indianstocks.

The analysis of efficiency and international integration is important for India as an emerging market for its development. Inprior literature, it is common to evaluate market indices for overall market efficiency. Through analysis of individual stocks as wellas overall market, this study contributes to existing literature with the most comprehensive evaluation of market efficiency andinternational independence for both of the Indian equity markets. In the following section, related previous literature issummarized. Data description and summary statistics are provided in the third section. The main evaluation of market efficiencyand independence is provided within the fourth section. Concluding remarks are the final section of the study.

3 Foreign trade data (dyadic trade data) is provided by; Barbieri, Katherine, Omar Keshk, and Brian Pollins, 2008, Correlates of War Project Trade Data SetCodebook, Version 2.0. Online: http://correlatesofwar.org. Also, Barbieri, Katherine, Omar M. G. Keshk, and Brian Pollins, forthcoming, “TRADING DATA:Evaluating our Assumptions and Coding Rules.” Conflict Management and Peace Science. Forthcoming. The version of the dataset used here is 2.01 and it isavailable through http://www.correlatesofwar.org/COW2%20Data/Trade/Trade.html.

4 Through “QuoteCenter” application of Equis International available at http://www.equis.com/.

http://www.correlatesofwar.org/COW2%20Data/Trade/Trade.html

http://correlatesofwar.org


http://www.equis.com/

315M.F. Dicle et al. / International Review of Economics and Finance 19 (2010) 313–339

2. Literature review

After Grubel (1968) several studies evaluated international financial markets' independence. Earlier studies of marketindependence find low relationships between markets (i.e. Ripley, 1973; Hilliard, 1979). However, studies of the 90s find more infavor of the existence of relationship especially from developed markets to developing markets and among regional markets (Eun& Shim, 1989; Cheung & Ng, 1992; Lee & Kim, 1994; Jeon & Von-Furstenberg, 1990; Cha & Oh, 2000). There are several potentialreasons for ‘independence of markets during earlier time periods’ and ‘increasing relationship during recent time periods’. AfterOctober 1987 crash, there is heightened research attention to the issue. Based on the studies that evaluate pre- and post-crisisperiod, there is also higher international dependence between markets after the crash, primarily due to markets becoming moreaware of other international markets and more cautious about the contagion between markets.

One of the earliest studies about Indian equity markets is the Sharma and Kennedy (1977) which compares BSE to the U.S. andto the U.K. markets. Through a runs test of market indices, they test the efficiency of the threemarkets for the time period between1963 and 1973 using monthly data. They present the initial evidence of efficiency and independence of the Indian market. Kumarand Mukhopadyay (2002) show Granger type causality from NASDAQ to Indian markets using intraday trading data for 1999–2001 period. Their study is the first to use intraday data to evaluate Indian markets' international integration. It is also one of thefirst studies that suggest predictability of Indian prices and market inefficiency. More importantly, Kumar and Mukhopadyay(2002) find volatility spillover from NASDAQ to the Indian markets. In support of short-term causality, Wong et al. (2004) provideevidence of Granger causality from the U.S., U.K. and Japan to BSE (200 index) for the 1991–2003 period using weekly data. Sarkaret al. (2008) evaluate BSE (30 large stocks) and two developed countries (U.S. and U.K.), two emerging countries (Argentina andBrazil) and a regional country (Indonesia) using daily data for 1999–2007 period. They find Granger type causality from developedmarkets to the Indian market for the volatility. They also find regional contagion effect.

In terms of the regional integration of the Indian markets, Nath and Verma (2003) evaluate the relationship between India(INE), Singapore and Taiwan for 1994–2002 period using daily data. Similar to the relationship with NASDAQ, they find no long-term relationship between these markets while they provide evidence in favor of weak causal effect. Bahng (2005) provideevidence for Granger causality from seven Asian countries to BSE of India for the 1990–2001 period using daily data. Interestingly,comparison of pre- and post-October 1987 crash shows that correlation between markets is higher after the crash.

While the results are mixed for the international integration of the Indian markets, there is no consensus for the efficiency ofINE and BSE. While the methodology and frequency of data vary significantly, it is common to use either overall market indices orindices comprised of only large stocks. Therefore, it could be possible to conclude the efficiency of the Indian markets, if there wasconsensus in prior literature. However, in terms of Indian markets offering diversification benefits for international investors, thefull potential of these markets has not been evaluated. The main contribution of this study, apart from evaluating the Indianmarkets' efficiencywithmost updated andmost extensive data, is to evaluate Indian stocks individually for possible diversificationbenefit for international investors. This type of evaluation is vital for the development of emerging markets. Attractinginternational investors depends primarily on two factors: market efficiency and international independence. While some stockscould have relationship to international markets, due to institutional trading or international operations, most other stocksmay beindependent and can offer diversification benefits.

3. Data

As part of international integration, more stringent regulation was aimed with the establishment of Securities and ExchangeBoard of India (SEBI) in 1988. Intended integration was not achieved until 1992, when Foreign Indirect Investment (FII) waspermitted. However, the step that should have the most important effect on international integration was taken in 2001 whenownership level of FII was increased to 49%. Thus, for the purpose of evaluating international independence andmarket efficiency,the time period starting with January, 2000 until the end of 2007 is evaluated.

Data for the study is provided by Dicle (2008)5 for individual ordinary6 stocks listed on all equity markets included in thisstudy. There are two types of filters employed: for daily observations and for stocks. Each daily observation must: 1) have end-of-day closing, bid and ask prices, 2) have bid and ask spread lower than 40% of the closing price and 3) must not have very high orvery low price. Excessive price filter is based on Chordia, Roll, and Subrahmanyam (2000) that excludes stocks cheaper than $2 forNYSE. While the $2 limit is reasonable for the U.S. markets, it does not have the same relevance for its Indian counterparts.Therefore, equally weighted market price is calculated for each of the five markets. In order to eliminate stocks with very high orvery low prices, any observation is excluded that falls below 1% or goes above 100 times the mean daily market price. In terms offilters for each stock, each one must: 1) have at least 40 observations for any given year and 2) have complete fundamental data,security type, currency and exchange. The final sample includes 36,525 ordinary stocks traded in 33 different countries and on 51different stock exchanges. The total market capitalization for the sample is about $73trillion using February 2008 exchange rates.

5 Data provided by Dicle (2008) is based on data provided by Reuters through “QuoteCenter” application of Equis International.6 Notes, bonds, options, futures, forwards, warrants, ETFs, trusts, mutual funds, REITs, depository receipts, profit sharing certificates, special drawing rights,

capital pools, shares of beneficial interest, suspended, delisted, inactive issues, inactive issuers, indices, index funds, income share, preferred shares, ADRs, GDR,CDIs, CHESS, ADS, commodities, gold coins, and gold certificates are excluded.


The analysis is based on returns that are calculated as;

7 Num8 For

zero) si9 Dic

Rc;i;t =ACPc;i;t−ACPc;i;t−1

ACPc;i;t

R stands for return, ACP for adjusted (for splits and dividends) end-of-day closing price, c for exchange, i for each individual
wherestock and t for each trading day. In the later parts of the study, to isolate the actual causality from onemarket to the other, differentcontrol variables are used. First, lagged return, Rc,i,t−1 is controlled to capture any autoregressive dependency. Second, liquidity iscontrolled to capture any relationship between returns and liquidity.
To measure liquidity, previous literature use several different measures including quoted spread (i.e. Amihud & Mendelson,1986; Chordia, Roll, & Subrahmanyam, 2001; Huberman & Halka, 2001; Eleswarapu, 1997), effective spread (i.e. Chalmers &Kadlec, 1998; Chordia et al., 2001), proportional spread (Chordia et al., 2000), and percentage spread (Chordia et al., 2000) anddepth7 (Huberman and Halka, 2001). The formulas for calculating these liquidity measures are as follows;

Si;t = ACPi;t;Ask−ACPi;t;Bid Quoted Spread

ESi;t = ACPi;t−MQi;t

MQi;t =ACPi;t;Ask−ACPi;t;Bid

2

Effective Spread

POSi;t =Spreadi;tMQi;t

Proportional Spread

PSi;t =Spreadi;tACPi;t

Percentage Spread

Since there is high correlation between different measures of liquidity, percentage spread is used to proxy for liquidity for thisstudy. Considering the higher variance of liquidity measures8 compared to returns, symmetric percentage daily change9 is usedinstead of ordinary percentage change. They are calculated as follows;

Lc;i;t =PSc;i;t−PSc;i;t−1

ðPSc;i;t−PSc;i;t−1Þ= 2Symmetric percentage change

PSc;i;t =PSc;i;t−PSc;i;t−1

PSc;i;t−1Ordinary percentage change :

The third control variable is the value weighted market return calculated as;

MRc;t = ∑ns

i=1Rc;i;t

MCc;i;t

TMCc;t

MC stands for market capitalization for stock i, TMC for the total market capitalization of the market, c and ns for the total
wherenumber of stocks traded in themarket) to capture themarket risk. Similarly, valueweighted change inmarket liquidity (percentagespread) is used to control for commonality in liquidity as reported by Chordia et al. (2001) and calculated as follows;
MLt = ∑ns

i=1Li;t

MCi;t

TMCt

There are also filters to eliminate outliers for each observation and for each stock. For the observation outliers, daily price,spread, percentage spread, return, and symmetric percentage change in spread are normalized (z-values). For the stock outliers,means for return, spread, percentage spread, and symmetric percentage change in spread are normalized (z-values). Only theobservations and stocks that fall within the 5 standard deviation fence are included in the analysis.

Table 2 provides the descriptive statistics for the daily value weighted market returns and value weighted symmetricalpercentage change in liquidity. Accordingly, while the meanmarket return is 0.058%, TSX Venture Exchange (Canada), Cairo Stock

ber of securities available at bid and ask prices.majority of stocks (especially for developing markets), liquidity measure increase (i.e. from near zero to 5–10%) and decrease (i.e. from 5–10% to neargnificantly. Using ordinary percentage understates downward movement.le (2008) shows the possible bias in using ordinary percentage change instead of symmetric percentage change for different liquidity measures.



Exchange, Jakarta Stock Exchange, Indian National Exchange and American Stock Exchange offer the highest mean daily returns.NASD OTC Bulletin Board (U.S.), Japan SDAQS, XETRA (Germany), LSE-AIM (U.K.) and KOSDAQ (Korea) offer the lowest mean dailyreturns. Since negative change in percentage spread means that the market is getting more liquid, Euronext Amsterdam, IndianNational Exchange, Lima Stock Exchange, Bombay Stock Exchange and Vienna Stock Exchange are the top five exchanges that havebeen gaining liquidity. Considering high levels of improvement in liquidity for the Indian markets, controlling for liquidity in theirrelationships with international markets is necessary. International Retail Service (U.K.), Stock Exchange of Singapore, StockExchange of Thailand, Berlin Stock Exchange and Hamburg Stock Exchange realized the lowest rates of improvement in marketliquidity.

4. Empirical models and findings

4.1. Market-wide contemporaneous relationship

Evaluation of international integration of Indian equity markets is the first step to measure diversification benefits the Indianmarkets offer for international investors. For this purpose, contemporaneous relationship is evaluated in returns; market-widevalue weighted return for each of the Indian equity markets are tested against value-weighted market returns for 49 other equitymarkets. Control variables for the evaluation include, lagged market return and value weighted change in market liquidity for theIndianmarkets. Additionally, lags of foreignmarket returns are tested for possible causality effects. LagrangeMultiplier (LM) test isemployed to measure the serial correlation and the appropriate lag length for autoregressive conditional heteroskedasticity(ARCH) model (Engle, 1982). Bollerslev (1986) generalizes the ARCHmodel by including variance and its lagged values. Based onevaluation10 of heteroskedasticity and AR components, GARCH (1, 1) model will be estimated with and AR (1) component.Following equation is estimated;

10 To c

MRISE;t = α0 + ∑2

j=0βjMRForeign;t−j + β3MRISE;t−1 + β4MLISE;t + εt

σ2t = γ0 + γ1ε

2t−1 + γ2σ

2t−1

εt jδt−1eNð0;σ2t Þ

ð1Þ

Results for the estimation of Eq. (1) are provided for BSE and INE in Tables 3 and 4 respectively. As an evidence of internationalintegration, almost all exchanges have statistically significant contemporaneous coefficients for market returns with both BSE and INEwhile SETS (U.K.), National Automated Trading (Australia), Stock Exchange of Singapore, Berlin Stock Exchange and Hong KongStock Exchange have the highest (positive) coefficients. Interestingly, both Chinese markets (Shanghai and Shenzhen) have thesmallest coefficients with BSE and statistically insignificant coefficients with INE. This is interesting considering the close traderelations between India and China. First lag of market returns is important due to different time zones with some of the developedcountries' equity markets. For instance, by the time Indian markets receive the information about the U.S. markets' trading, it is anew trading day in India. It is therefore reasonable to expect American markets to effect Indian markets with a lag. In line with ourexpectations, New York Stock Exchange, Toronto Stock Exchange, Santiago Stock Exchange, Hamburg Stock Exchange andNASDAQ (U.S.) have the highest lag return coefficients with BSE and INE. There are 25 markets with statistically significant laggedreturn with BSE and INE. Interestingly the lagged return coefficient is negative for Korea Stock Exchange, KOSDAQ (Korea), TaiwanStock Exchange, Jakarta Stock Exchange, National Automated Trading (Australia), Hong Kong Stock Exchange and Japan SDAQSwith both Indian markets.

These results point to significant international integration of both Indian markets with the rest of the World. The positivecoefficients point to the fact that Indian markets are in-line with international equity trading. While the market-wide relationshipis contemporaneous, there is significant relationship with major international equity markets with a trading day lag, possibly dueto time-zone differences. These results are robust to lagged Indian market return, Indian market liquidity and any GARCH (1, 1)effect that the returns have.

At this stage, special emphasis is paid to the relationship between Indian markets and US markets considering the increasedtrade and FDI volumes between these two countries. US markets, especially the NYSE, are among the most important equitymarkets in the World. Thus, it is expected of international markets to be correlated with the NYSE and possible with otherAmerican equity markets. Also, even the changes to the US economic indicators such as discount rates have an effect oninternational equity markets (i.e. Johnson & Jensen, 1993). A comparison of relationship between Indian and US markets for twodifferent time periods (earlier vs. more recent) would provide evidence of change in the level of integration. For this purpose,Eq. (1) is estimated for two different time periods, 2000–2003 and 2004–2007. The intention is to evaluate whether increasedtrade and FDI cooperation between the two countries leads to higher equity market correlations. Table 5 provides the results ofthis analysis. Accordingly, in terms of BSE, the contemporaneous coefficient is not statistically significant for the 2000–2003 but itis significant for the 2004–2007. Coefficients are much higher for the 2004–2007 compared to the 2000–2003. Also, for NASDAQ,the contemporaneous coefficient is 0.0489 for the earlier period and 0.2194 for the later period. A similar pattern is also observed

onserve space, these evaluations are not provided in tables. They are available upon request.




for the INE and for the coefficients of the lags. The increased economic relationship between India and the US has evidentlyreflected as the increased correlation between the equity markets of the two countries.

4.2. Testing day-of-the-week effect

Considering the evidence provided with the existing literature on the day-of-the-week, further analysis tests whether the day-of-the-week effect is due to spillovers from foreign equity markets. With this analysis, Jaffe andWesterfield (1985) argument thatthe day-of-the-week effect may be related to integration with the U.S. equity markets is tested. Initially, day-of-the-week effect istested without including the value weighted market returns for international markets. For this purpose, Eq. (1) is extended byincluding daily dummy variables forMondays, Tuesdays, Thursdays and Fridays. Each daily dummy variable (D) is assigned a valueof one for the corresponding day and zero otherwise. Then, the amended model is estimated by including the internationalmarkets' returns to check whether day-of-the-week effect can be explained. Following equation is estimated;


j=1βjMRForeign;t−j + β3MRISE;t−1 + β4MLBSE;t + ∑

8

j=5βjDj;t + εt

σ2t = γ0 + γ1ε

2t−1 + γ2σ

2t−1


: ð2Þ

Results for the estimation of Eq. (2) are provided for BSE and INE in Tables 6 and 7 respectively. The estimation of Eq. (2)without the international market returns (∑j=0

2 βjMRForeign,t−j), provides a base for comparison; while only the coefficient forMonday (−0.002) is statistically significant for BSE, coefficients for Monday (−0.0034) and Friday (−0.0014) are both statisticallysignificant for INE. Controlling for foreign market returns as possible reasons for DOW effect in the Indian markets, Monday'scoefficient becomes statistically insignificant for 36 of the 49 exchanges for BSE. For BSE, the DOW effect seems to be an internationalspillover. In terms of the INE, the DOW effect for Monday disappears with the inclusion of six of the exchanges, two of which are JapanSDAQS and NASDAQ Japan. The Friday effect disappears with the inclusion of 13 exchanges. Both days disappear with includingmarket returns for Santiago Stock Exchange, Cairo Stock Exchange and NASDAQ Japan. Thus, for both of the Indian markets, we canconclude that the previously reported DOW effect can be explained by the Indian markets' integration with the international equitymarkets.

4.3. Market-wide Granger causality

Since the initial evidence points to statistically significant lagged coefficients for foreign market returns, Granger causality isexamined in order to determine whether the Indian markets are caused by any of the regional markets and/or influentialdeveloped markets. With this analysis, evidence provided by prior literature (Kumar & Mukhopadyay, 2002; Wong et al., 2004;Bahng, 2005 and Sarkar et al., 2008) is tested.

Granger causality is a test to evaluate whether a variable, with all of its lags, cause another variable. Causality is establishedwith aWald test, which is a combined significance test for the causing variable's lags. The null hypothesis is that a variable, with allof its lags, does not Granger cause a dependent variable. Following VAR model is estimated for the evaluation of Grangercausality:


j=1βjMRForeign;t−j + ∑

3

j=1φ4MRISE;t−j + φ4MLISE;t + εt

σ2t = γ0 + γ1ε

2t−1 + γ2σ

2t−1


: ð3Þ

The null hypothesis for the Wald test for the Granger causality is: β1=β2=β3=0. In other words, international markets'returns, with all of their lags, do not Granger cause Indian markets' returns. Results for the estimation of Eq. (3) are provided forBSE and INE in Tables 8 and 9 respectively. After controlling for lagged market returns for Indian markets themselves (within theVAR context) , lagged market returns for foreign equity markets remains to be statistically significant; for BSE (INE), 24 (26)markets at the first lag, 18 (10) markets at the second lag and 6 (8) markets at the third lag. 26 of the 49 equity markets' returnsGranger cause BSE returns with at least 5% significance. In terms of BSE, causing regional markets that are at an earlier time zoneinclude; Hong Kong Stock Exchange, Tokyo Stock Exchange, Japan SDAQS, New Zealand Stock Exchange and Taiwan StockExchange. Causing international markets that are at a later time zone include; both Canadian exchanges, five of the Germanexchanges, SETS (U.K.), International Retail Service (U.K) and all U.S. markets other than NASD OTC Bulletin Board.

In terms of the INE, 18 of the 49 equity markets' returns Granger cause INE returns with at least 5% significance.While Vienna StockExchange, TSX Venture Exchange (Canada), Santiago Stock Exchange, Berlin Stock Exchange, Munich Stock Exchange, FrankfurtStock Exchange, Hamburg Stock Exchange, Barcelona Stock Exchange and Taiwan Stock Exchange Granger cause BSE, they do notcause INE. On the other hand, NASDAQ Japan Granger cause INE and does not cause BSE.




Overall results indicate that the returns for Indian equity markets are Granger caused by some of the regional markets and some ofthe influential developed markets. While this is an important evidence for Indian markets international integration, it is alsoevidence against diversification benefits Indian markets offer for international investors.

4.4. Individual stocks' Granger causality

While the evidence points to market-wide causality from international markets towards Indian markets, in terms of individualstocks, certain types of stocks may be responsible for market-wide causal relationship. For instance, the previous literatureprovides evidence for the preference of past winners by institutional investors (i.e. Grinblatt et al., 1995; Nofsinger & Sias, 1999and Badrinath &Wahal, 2002). There is also evidence for international institutional investors' preference for larger andmore liquidstocks (i.e. Gompers & Metrick, 1999 and Ferreira & Matos, 2008). Thus, if it can be established that only certain stocks traded onIndian markets are to blame for market-wide causality with international markets, then it could be concluded that some otherIndian stocks still offer diversification benefits for international investors. In order to test this hypothesis, VAR model is estimatedon panel data consisting of all traded stocks in each of the Indian markets. In order to account for variance in returns, difference ofsquared returns (ΔR2) is included as a control variable. The estimated VAR model is as follows;

11 A rutwo, it

RISE;i;t = α0 + ∑3

j=1βjRISE;i;t−j + β4MRISE;i;t + β5ΔR

2ISE;i;t + β6LISE;i;t

+ ∑3

j=1ϑjMRForeign;t−j + εi;t + ϑi

: ð4Þ

In order to check for Granger type causality, Eq. (4) is amended to be estimated for each of the listed stocks separately. Aftereach of the estimations, a Wald test is employed to test the hypothesis that the international equity market's returns with all of itslags Granger cause Indian stocks' returns (ϑ1=ϑ3=ϑ3=0). The model that is estimated for each Indian stock individually is asfollows;

RISE;i;t = α0 + ∑3

j=1βjRISE;i;t−j + β4MRISE;i;t + β5ΔR

2ISE;i;t + β6LISE;i;t

+ ∑3

j=1ϑjMRForeign;t−j + εi;t

ð5Þ

Results for the estimation of Eqs. (4) and (5) are provided for BSE and INE in Tables 10 and 11 respectively. While all thecoefficients for Eq. (4) is provided, to conserve space, only the percentage of each Indianmarket that has statistically significant (at1%, 5% and 10% separately)Wald test statistic for Granger causality from international markets to the Indianmarkets are provided.Accordingly, at 5% statistical significance, less than 8% of each of the Indian markets is Granger caused by individual internationalmarkets, after controlling for their market returns, return variances, liquidities. These results are important for internationalinvestors around theworld. It implies that, for investors in each country, there is potential for diversification through Indian stocks.For instance, for investors in Austria, at 5% significance, 5.50% of BSE is caused by Vienna Stock Exchange. While overall BSE isGranger caused by Vienna Stock Exchange, only 5.50% of BSE stocks are to blame. Thus, the other stocks traded on BSE offerdiversification benefits for Austrian investors.

However, in terms of the global diversification benefits that Indian equity markets offer, number of Indian stocks that areGranger caused by at least one international market is counted. 86% of BSE and 91% of INE are Granger caused by at least oneinternational market even after controlling for their liquidity andmarket returns. Thus, it is concluded thatmost of the Indian stockshave strong correlations and causality with international markets. Four of the markets that have the highest influence (>11.5%) onthe individual Indian stocks include; Hong Kong Stock Exchange, National Automated Trading (Australia), Toronto Stock Exchangeand NASDAQ (U.S.).

4.5. Runs test

Randomness of returns for individual stocks traded in Indian markets is tested with runs test11 similar to Sharma and Kennedy(1977). The main difference is the significance test of the different runs lengths for which Monte Carlo study is used to generatemean and standard deviation for z-scores that is calculated as: z = x− x�

σxwhere x is the number of runs, x

_is the mean number of

n is defined as consecutive returns with the same sign. A run of two means, three consecutive trading days with the same sign. If a specific runs length isis basically two consecutive runs of one, however, they are not counted towards runs of one.



runs and σx is the standard deviation of runs for each runs length. For the Monte Carlo study, 100 to 2,000 observations aregenerated randomly between negative one and positive one. Runs are counted for each runs length up to runs of 15. Thus, for eachruns length there is a total number of runs. A sum of total number of runs for all runs lengths is also calculated. This procedure isrepeated 10,000 times. Resulting Monte Carlo data contains 10,000 of the number of runs for each runs length, for all lengths andfor all number of observations from 100 to 2000. The mean number of runs for each length x ̅ and standard deviation for each runslength (σx) are used to calculate the z-score for that specific runs length.

For each traded stock in both of the Indian markets, number of runs is counted for each runs length (up to runs of 15) and a z-score is calculated to test its significance using the mean and standard deviation obtained with the Monte Carlo study. The nullhypothesis tested is the randomness of the return series. Table 12 provides the results for this analysis. In this table, “mean12” iscalculated as the cross-sectional average of number of runs divided by the number of observations for each stock. For instance,11.32% mean for a runs length of one refers to 170 runs of one for a stock with 1500 observations. Accordingly, the mean is 11.32%for BSE and 11.94% for INE for runs of one. It drops down to about 5.05% for runs of two and to 2.3% for runs of three. Any run that ishigher than four has less than 1% for both markets.

The results of significance tests are provided at 1%, 5% and 10%. The percentages in corresponding columns at Table 12 refer topercentage of market that have statistically significant runs test (non-random returns). In terms of BSE, for runs length of one,returns are not random for 18.62% of the market at 1% significance. The percentage of market with non-random returns increasesto 31.42% for runs of two, to 29.04% for runs of three and to 20.43% for runs of four. At 10% significance, 50.37% of the market hasruns of three and therefore non-random. In terms of INE, the levels are significantly lower compared to BSE. At 1% statisticalsignificance, only 10.56% of the market has runs of one, 12.21% has runs of two, 11.2% has runs of three and 7.59% has runs of four.Considering all runs lengths at 1% statistical significance, BSE has 57.83% of the market with non-random returns whereas INE has26.73%. In sum, INE seems to bemore efficient compared to BSE. However, overall the evidence providedwith the runs test point toquestionable market efficiency for the Indian markets.

5. Conclusion

The purpose of this study is to evaluate the two Indian equity markets, Bombay Stock Exchange (BSE) and Indian NationalExchange (INE), for their efficiency and for diversification benefits they offer to international investors. The internationalintegration is measured with an evaluation of contemporaneous relationship of returns between the Indian and internationalmarkets. Statistically significant relationship is found with almost all equity markets included in the study with an averagecontemporaneous coefficient of 0.209 for BSE and 0.190 for INE. While the number of markets with significant lagged correlationin returns is lower, it is still a considerable number (25 of 49) for both of the Indianmarkets. Thus, Granger causality is estimated tomeasure the causal effect that international markets have on the Indian markets. Accordingly, 26 (18) of the 49 equity markets'returns Granger cause BSE (INE) returns with at least 5% significance. The results of these two tests lead to the conclusion that bothIndian markets are strongly integrated into the international markets and therefore diversification benefits offered to internationalinvestors is minimal or even questionable. Since considerable number of international markets Granger cause Indian markets, thepredictability of returns may be possible and therefore questionable market efficiency and strong evidence for market dependence. Basedon the day-of-the-week effect evidence without controlling for international markets, the evidence provided in this study pointsto spillover from international markets. Such result contributes positively for the Indian markets' efficiency by providing anexplanation for a possible anomaly.

To further the analysis of market efficiency and international dependence, individual stocks are evaluated for whether theirreturns are Granger caused by the international markets or not. The results point to majority (86% of BSE and 91% of INE) of Indianstocks to be Granger caused by at least one international market even after controlling for market returns, liquidity and returnvariance. This evidence is in support of the argument that most of the Indian stocks have strong dependence on internationalmarkets while there may be possibility of diversification benefits through small number of stocks that do not have causalrelationship with the international markets.

The diversification benefits offered by Indian equity markets are limited for global portfolios. However, from individualcountries' perspective there is opportunity for diversification through Indian equities. While individual countries may Grangercause market returns, on average 7% of individual Indian stocks are to blame.

In terms of the predictability of Indian stocks' returns, hence Indian market efficiency, it is established that the Indian marketshave strong contemporaneous and causal dependence on international equity markets. It may be possible to predict Indian stocks'returns based on the returns of other markets. In further evidence of return predictability, results of runs test reveal that, at 1%statistical significance, BSE (INE) has 57.83% (26.73%) of the market with non-random returns. Thus, it may be possible to predictIndian stocks' returns based on such non-randomness.

In sum, this study finds that Indian markets are strongly integrated with the international markets, evidence against possibleinternational diversification benefits. The efficiency of the Indianmarkets is questionable since returnsmay be predictable based oninternational market returns and momentum trading strategies. INE seems to be, however, more efficient and less internationallyintegrated compared to BSE.

12 Usual runs test use the number of runs for each length. However, since this study evaluates each stock individually, the meaning of number of runs dependson the number of observations for each stock. For instance, runs of 10 for a stock with 1,000 observations would be quite insignificant, however, it would besignificant for a newly listed stock with 100 observations.




Appendix A

Table 1Foreign trade figures for India for 2006.

Trade Partner
Exports Imports
(continued

Total

United States of America
$22,992.70 $11,100.40 $34,093.10 China $14,786.50 $19,300.60 $34,087.10 United Arab Emirates $11,246.00 $5524.53 $16,770.53 Germany $4671.29 $8459.63 $13,130.92 United Kingdom $5857.47 $5492.35 $11,349.82 Belgium $3,796.63 $6391.83 $10,188.46 Japan $4117.08 $4934.62 $9051.70 Switzerland $611.72 $7165.02 $7776.74 South Korea $2610.37 $5110.22 $7720.59 Italy $3746.82 $3011.13 $6757.95 France $2259.94 $3495.53 $5755.47 Saudi Arabia $2268.09 $1933.97 $4202.06 Russia $967.11 $3197.67 $4164.78 Netherlands $2371.61 $1561.70 $3933.31 Canada $1853.71 $1585.77 $3439.48 Spain $2135.42 $729.00 $2864.42 Israel $1433.30 $1397.44 $2830.74 Taiwan $1243.17 $1448.55 $2691.72 Brazil $1635.14 $1,045.70 $2680.84 South Africa $1745.00 $860.39 $2605.39 Iran $1616.18 $775.62 $2391.80 Mexico $1238.71 $748.33 $1987.04 Sweden $443.83 $1480.78 $1924.61 Turkey $1,558.98 $244.47 $1803.45 Chile $164.50 $1,638.11 $1802.61 Qatar $326.77 $958.61 $1285.38 Ukraine $397.16 $872.08 $1269.24 Nigeria $1,142.83 $83.24 $1226.07 Kuwait $660.15 $530.36 $1190.51 Argentina $323.76 $818.74 $1142.50 Egypt $828.47 $249.21 $1077.68
Exports refers to the exports to the trade partner country and Imports refer to the imports from the trade partner country. Figures are in current US dollars inmillions. Data is provided by: Barbieri, Katherine, Omar Keshk, and Brian Pollins, 2008, Correlates of War Project Trade Data Set Codebook, Version 2.0. Online:http://correlatesofwar.org. Also, Barbieri, Katherine, Omar M. G. Keshk, and Brian Pollins, forthcoming, “TRADING DATA: Evaluating our Assumptions and CodingRules.” Conflict Management and Peace Science. Forthcoming. The version of the dataset used here is 2.01 and it is available through http://www.correlatesofwar.org/COW2%20Data/Trade/Trade.html.

Table 2Descriptive statistics for the equity markets included in the study.

Country
Exchange VW. Average of Returns VW. Average of Change in Liquidity
n
Mean Median Std.Dev.
Skew.
Kurt. Mean Median Std.Dev.
Skew.

on ne

Kurt.

Australia
National Automated Trading 1998 0.080% 0.109% 0.0078 −1.65 22.47 −1.851% −1.609% 0.1472 −0.06 3.30 Austria Vienna Stock Exchange 1951 0.019% 0.022% 0.0088 −0.21 11.64 −2.651% −1.168% 0.2373 −0.65 5.89 Belgium Euronext Brussels 1100 0.102% 0.013% 0.0226 1.43 22.33 −1.730% −1.403% 0.3227 −0.04 7.15 Canada TSX Venture Exchange 2005 0.311% 0.336% 0.0119 −0.35 7.22 −2.200% −2.252% 0.0683 −0.08 5.24 Canada Toronto Stock Exchange 2016 0.090% 0.099% 0.0093 0.20 8.22 −2.384% −1.808% 0.1993 −0.08 3.36 Chile Santiago Stock Exchange 1398 0.088% 0.094% 0.0062 −0.17 4.71 −1.862% −1.555% 0.1970 −0.69 9.07 China Shanghai Stock Exchange 1872 0.099% 0.113% 0.0141 0.23 6.35 −1.661% −1.389% 0.0598 −0.38 6.43 China Shenzhen Stock Exchange 1874 0.102% 0.133% 0.0154 0.06 6.04 −1.725% −1.704% 0.0602 −0.19 4.61 Denmark Copenhagen Stock Exchange 1972 0.080% 0.127% 0.0102 −0.35 5.55 −1.121% −1.078% 0.1841 −0.09 3.66 Egypt Cairo Stock Exchange 1256 0.176% 0.189% 0.0136 −0.42 5.72 −2.320% −0.698% 0.2343 −0.11 3.30 Finland Helsinki Stock Exchange 1987 0.072% 0.112% 0.0206 0.10 5.58 −2.490% −1.061% 0.4020 −0.27 4.25 France Euronext Paris 959 0.020% 0.039% 0.0144 0.17 4.54 −2.403% −2.212% 0.2283 0.00 3.10 Germany Berlin Stock Exchange 2006 0.055% 0.071% 0.0076 −0.22 5.32 −0.301% −0.230% 0.0167 −0.54 6.72 Germany Stuttgart Stock Exchange 1410 0.077% 0.000% 0.0177 0.47 14.64 −1.001% −0.067% 0.3640 −0.27 5.24 Germany Munich Stock Exchange 2006 0.041% 0.059% 0.0083 −0.07 4.27 −0.840% −0.140% 0.0955 −1.72 27.54 Germany Frankfurt Stock Exchange 2006 0.023% 0.030% 0.0109 0.11 5.45 −1.143% −1.018% 0.1399 −0.10 3.76 Germany XETRA 2001 −0.037% −0.002% 0.0160 −0.02 7.77 −2.213% −0.612% 0.3758 −0.16 4.24 Germany Duesseldorf Stock Exchange 2006 0.034% 0.048% 0.0128 0.21 5.68 −0.775% −0.303% 0.0447 −0.67 20.09 Germany Hamburg Stock Exchange 2006 0.064% 0.052% 0.0095 −0.01 4.05 −0.205% −0.129% 0.0317 −0.09 15.50 Greece Athens Stock Exchange 1969 0.002% 0.041% 0.0135 −0.33 6.43 −1.763% −1.857% 0.1709 0.06 3.29 Hong Kong Hong Kong Stock Exchange 1930 0.040% 0.080% 0.0122 −0.58 9.14 −1.519% −1.145% 0.1151 −0.13 5.00
xt page)

http://correlatesofwar.org


(continued)Table 2 (continued)

Country
Exchange VW. Average of Returns VW. Average of Change in Liquidity
n
Mean Median Std.Dev.
Skew.
Kurt. Mean Median Std.Dev.
Skew.
Kurt.
India
Bombay Stock Exchange 2003 0.112% 0.243% 0.0145 −0.68 5.61 −2.773% −3.369% 0.1651 0.14 3.57 India Indian National Exchange 1974 0.139% 0.231% 0.0144 −0.57 5.56 −2.840% −2.861% 0.2249 0.03 3.42 Indonesia Jakarta Stock Exchange 1906 0.171% 0.197% 0.0157 −0.21 5.90 −0.880% −0.765% 0.0691 −0.18 3.76 Italy Milano Stock Exchange 1999 0.034% 0.079% 0.0104 −0.23 5.77 −1.494% −1.189% 0.1543 −0.24 4.31 Japan Tokyo Stock Exchange 1925 0.050% 0.053% 0.0133 0.24 8.91 −1.885% −1.435% 0.2284 −0.62 12.33 Japan Osaka Stock Exchange 1736 0.072% 0.015% 0.0114 0.16 5.02 −1.505% −0.108% 0.2222 −0.11 3.82 Japan Japan SDAQS 1943 −0.022% 0.025% 0.0130 −0.36 7.84 −1.985% −1.717% 0.1070 −0.10 4.26 Japan NASDAQ Japan 1717 −0.104% −0.069% 0.0219 −0.55 7.24 −1.103% −1.643% 0.2111 0.31 7.52 Jordan Amman Financial Market 1531 0.076% 0.079% 0.0088 −0.16 5.22 −2.075% −0.910% 0.1938 −0.04 3.40 Korea Korea Stock Exchange 1944 0.035% 0.181% 0.0180 −0.53 5.21 −1.444% −0.837% 0.1267 −0.32 5.26 Korea KOSDAQ 1871 −0.085% 0.099% 0.0206 −0.67 8.27 −1.417% −1.310% 0.1065 0.15 5.07 Malaysia Kuala Lumpur Stock Exchange 1953 0.043% 0.034% 0.0101 −0.13 9.10 −1.051% −0.898% 0.0616 −0.52 10.98 Netherlands Euronext Amsterdam 1175 0.012% 0.066% 0.0171 0.19 4.24 −2.933% −2.788% 0.2561 −0.14 3.89 New Zealand New Zealand Stock Exchange 1977 0.043% 0.055% 0.0082 −0.22 9.52 −2.260% −2.622% 0.2481 0.00 2.74 Norway Oslo Stock Exchange 1972 0.031% 0.114% 0.0271 −0.30 17.28 −0.867% −0.383% 0.2920 −0.02 6.11 Peru Lima Stock Exchange 1964 0.123% 0.022% 0.0093 0.50 11.19 −2.827% −1.447% 0.3126 −0.15 4.55 Singapore Stock Exchange of Singapore 1997 0.070% 0.102% 0.0110 −0.34 6.05 −0.647% −0.371% 0.0646 −0.25 4.50 South Africa Johannesburg Stock Exchange 1974 0.106% 0.127% 0.0113 −0.21 5.18 −2.407% −2.020% 0.2148 0.03 2.88 Spain Barcelona Stock Exchange 1990 0.034% 0.083% 0.0118 −0.20 4.45 −1.922% −1.438% 0.1623 −0.30 4.70 Sweden Stockholm Stock Exchange 1982 0.046% 0.097% 0.0132 −0.08 5.55 −0.999% −0.770% 0.0990 −0.07 5.06 Switzerland CHE SWX Swiss Exchange 1993 0.032% 0.085% 0.0105 −0.28 6.22 −2.126% −1.672% 0.1587 −0.35 6.57 Taiwan Taiwan Stock Exchange 1971 0.005% 0.056% 0.0140 −0.27 4.94 −0.922% −0.677% 0.0555 −0.64 10.84 Thailand Stock Exchange of Thailand (SET) 1932 0.060% 0.056% 0.0144 −0.13 5.67 −0.504% −0.402% 0.0453 −0.34 5.36 United Kingdom SETS (Electronic Trading Service) 1993 0.063% 0.116% 0.0081 −0.65 6.02 −1.968% −1.945% 0.1172 −0.01 4.39 United Kingdom LSE-AIM 1991 −0.039% 0.018% 0.0141 −0.11 9.02 −0.854% −0.794% 0.1005 −0.05 6.24 United Kingdom International Retail Service (IRS) 1899 0.014% 0.041% 0.0128 0.41 9.43 −0.663% −0.054% 0.1027 −2.77 37.73 United States New York Stock Exchange 2005 0.069% 0.100% 0.0096 0.06 5.06 −1.490% −0.996% 0.1199 −0.30 3.85 United States NASD OTC Bulletin Board 2008 −0.003% −0.017% 0.0327 −0.04 19.16 −1.707% −0.680% 0.2303 −0.15 6.79 United States American Stock Exchange 2005 0.130% 0.134% 0.0129 1.18 19.02 −2.133% −1.459% 0.1944 −0.09 3.31 United States Nasdaq 2008 0.109% 0.175% 0.0183 0.24 6.32 −2.219% −2.012% 0.0997 −0.28 6.35
n refers to number of observations, VW refers to value weighted market return (MRt) or market liquidity (MLt), std.dev. refers to standard deviation, skew refers toskewness and kurt refers to kurtosis. Data for the study is provided by Dicle (2008) based on Reuters through “QuoteCenter” application of Equis International forthe time period starting with January, 2000 until the end of 2007.

Table 3Results for market-wide relationship between for Bombay Stock Exchange (BSE) returns and international equity markets' returns using Eq. (1).

Dependent: Bombay Stock Exchange

Exchange
Country Constant MRother,t MRother,t−1 MRother,t−2 MRt−1 MLt ArchCons.
Arch
Garch Chi2
National AutomatedTrading

Australia
0.0006 * 0.526 *** −0.072 * 0.012 0.167 *** −0.018 *** 0.0000 *** 0.231 *** 0.650 *** 302.26 ***
Vienna StockExchange

Austria
0.0007 ** 0.073 * 0.029 0.062 0.180 *** −0.019 *** 0.0000 *** 0.274 *** 0.572 *** 171.65 ***
Euronext Brussels
Belgium 0.0004 0.130 *** −0.006 0.025 0.174 *** −0.015 *** 0.0000 *** 0.257 *** 0.619 *** 138.54 *** TSX VentureExchange
Canada
0.0001 0.190 *** 0.083 *** −0.005 0.168 *** −0.019 *** 0.0001 *** 0.301 *** 0.381 *** 224.57 ***
Toronto StockExchange

Canada
0.0007 ** 0.281 *** 0.195 *** 0.000 0.155 *** −0.018 *** 0.0000 *** 0.279 *** 0.526 *** 303.38 ***
Santiago StockExchange

Chile
0.0006 0.267 *** 0.177 *** −0.021 0.200 *** −0.026 *** 0.0000 * 0.292 *** 0.427 ** 215.78 ***
Shanghai StockExchange

China
0.0011 *** 0.057 ** −0.007 0.047 * 0.151 *** −0.018 *** 0.0001 *** 0.340 *** 0.380 *** 149.08 ***
Shenzhen StockExchange

China
0.0012 *** 0.043 * −0.014 0.030 0.150 *** −0.018 *** 0.0001 *** 0.335 *** 0.390 *** 142.56 ***
Copenhagen StockExchange

Denmark
0.0010 *** 0.307 *** 0.026 0.007 0.151 *** −0.018 *** 0.0001 *** 0.362 *** 0.321 *** 326.24 ***
Cairo StockExchange

Egypt
0.0010 ** 0.098 *** −0.038 0.023 0.061 ** −0.028 *** 0.0000 0.278 *** 0.675 *** 106.07 ***
Helsinki StockExchange

Finland
0.0007 ** 0.113 *** 0.046 *** 0.001 0.166 *** −0.018 *** 0.0000 *** 0.262 *** 0.572 *** 233.48 ***
Euronext Paris
France 0.0008 * 0.155 *** 0.046 0.003 0.153 *** −0.011 *** 0.0000 *** 0.329 *** 0.491 *** 77.89 *** Berlin StockExchange
Germany
0.0009 ** 0.409 *** 0.093 ** −0.004 0.147 *** −0.018 *** 0.0000 *** 0.274 *** 0.528 *** 287.21 ***
Stuttgart StockExchange

Germany
0.0011 *** 0.088 *** 0.024 0.010 0.176 *** −0.026 *** 0.0000 0.231 *** 0.631 *** 155.91 ***


(continued)


Table 3 (continued)

Exchange
Country Constant MRother,t MRother,t−1 MRother,t−2 MRt−1 MLt ArchCons.
Arch
Garch Chi2
Munich StockExchange

Germany
0.0009 ** 0.337 *** 0.054 0.011 0.157 *** −0.018 *** 0.0000 *** 0.259 *** 0.556 *** 258.57 ***
Frankfurt StockExchange

Germany
0.0011 *** 0.211 *** 0.036 −0.057 * 0.153 *** −0.018 *** 0.0000 *** 0.276 *** 0.493 *** 206.64 ***
XETRA
Germany 0.0011 *** 0.089 *** 0.033 * −0.019 0.180 *** −0.018 *** 0.0001 *** 0.311 *** 0.432 *** 209.56 *** Duesseldorf StockExchange
Germany
0.0010 *** 0.254 *** 0.064 *** 0.026 0.153 *** −0.018 *** 0.0000 *** 0.276 *** 0.553 *** 325.96 ***
Hamburg StockExchange

Germany
0.0008 ** 0.220 *** 0.139 *** −0.027 0.152 *** −0.018 *** 0.0000 *** 0.284 *** 0.534 *** 247.44 ***
Athens StockExchange

Greece
0.0012 *** 0.197 *** −0.002 0.046 ** 0.169 *** −0.018 *** 0.0001 *** 0.357 *** 0.187 ** 319.29 ***
Hong Kong StockExchange

Hong Kong
0.0008 ** 0.399 *** −0.084 *** 0.049 ** 0.141 *** −0.018 *** 0.0000 ** 0.217 *** 0.646 *** 468.52 ***
Jakarta StockExchange

Indonesia
0.0005 0.191 *** −0.079 *** −0.036 * 0.184 *** −0.018 *** 0.0000 *** 0.254 *** 0.569 *** 250.58 ***
Milano StockExchange

Italy
0.0009 ** 0.249 *** 0.113 *** −0.012 0.161 *** −0.019 *** 0.0001 *** 0.283 *** 0.436 *** 262.25 ***
Tokyo StockExchange

Japan
0.0011 *** 0.191 *** 0.047 * 0.028 0.140 *** −0.019 *** 0.0001 *** 0.311 *** 0.353 *** 212.29 ***
Osaka StockExchange

Japan
0.0008 ** 0.254 *** 0.013 0.012 0.169 *** −0.022 *** 0.0001 *** 0.261 *** 0.425 *** 248.38 ***
Japan SDAQS
Japan 0.0009 ** 0.219 *** −0.071 *** 0.041 * 0.141 *** −0.020 *** 0.0000 *** 0.218 *** 0.604 *** 182.40 *** NASDAQ Japan Japan 0.0011 *** 0.084 *** −0.005 0.003 0.177 *** −0.023 *** 0.0001 *** 0.234 *** 0.242 214.86 *** Amman FinancialMarket
Jordan
0.0009 ** 0.137 *** 0.015 0.065 0.050 ** −0.021 *** 0.0000 *** 0.306 *** 0.393 *** 115.16 ***
Korea StockExchange

Korea
0.0011 *** 0.246 *** −0.036 * −0.016 0.130 *** −0.017 *** 0.0000 *** 0.217 *** 0.517 *** 347.91 ***
KOSDAQ
Korea 0.0012 *** 0.184 *** −0.041 *** −0.021 0.146 *** −0.019 *** 0.0001 *** 0.197 *** 0.474 *** 297.85 *** Kuala Lumpur StockExchange
Malaysia
0.0005 0.247 *** −0.038 −0.015 0.167 *** −0.019 *** 0.0000 ** 0.238 *** 0.669 *** 220.79 ***
EuronextAmsterdam

Netherlands
0.0005 0.177 *** 0.025 −0.010 0.159 *** −0.017 *** 0.0000 *** 0.246 *** 0.637 *** 154.35 ***
New Zealand StockExchange

New Zealand
0.0008 ** 0.256 *** −0.028 0.102 ** 0.163 *** −0.017 *** 0.0000 *** 0.277 *** 0.572 *** 223.95 ***
Oslo Stock Exchange
Norway 0.0009 ** 0.026 ** 0.012 −0.014 0.177 *** −0.018 *** 0.0000 *** 0.252 *** 0.561 *** 156.30 *** Lima StockExchange
Peru
0.0005 0.086 ** 0.069 * 0.013 0.171 *** −0.019 *** 0.0000 *** 0.279 *** 0.570 *** 193.42 ***
Stock Exchange ofSingapore

Singapore
0.0004 0.485 *** 0.019 −0.001 0.149 *** −0.017 *** 0.0000 *** 0.213 *** 0.601 *** 449.23 ***
Johannesburg StockExchange

South Africa
0.0009 *** 0.341 *** 0.066 ** 0.082 *** 0.130 *** −0.016 *** 0.0000 *** 0.335 *** 0.455 *** 425.03 ***
Barcelona StockExchange

Spain
0.0010 *** 0.246 *** 0.074 *** 0.005 0.156 *** −0.018 *** 0.0000 *** 0.325 *** 0.473 *** 286.26 ***
Stockholm StockExchange

Sweden
0.0009 *** 0.234 *** 0.093 *** 0.030 0.135 *** −0.018 *** 0.0000 *** 0.264 *** 0.567 *** 272.17 ***
CHE SWX SwissExchange

Switzerland
0.0010 *** 0.304 *** 0.023 0.037 0.155 *** −0.018 *** 0.0000 *** 0.292 *** 0.528 *** 316.32 ***
Taiwan StockExchange

Taiwan
0.0008 ** 0.215 *** −0.056 ** −0.008 0.166 *** −0.019 *** 0.0000 ** 0.245 *** 0.650 *** 229.37 ***
Stock Exchange ofThailand (SET)

Thailand
0.0005 0.200 *** −0.007 −0.023 0.193 *** −0.019 *** 0.0000 ** 0.241 *** 0.658 *** 229.57 ***
SETS (ElectronicTrading Service)

UnitedKingdom

0.0007 **
0.531 *** 0.087 ** 0.023 0.152 *** −0.017 *** 0.0000 *** 0.299 *** 0.485 *** 437.39 ***
LSE-AIM
UnitedKingdom
0.0009 **
0.129 *** 0.035 −0.006 0.168 *** −0.019 *** 0.0000 *** 0.272 *** 0.521 *** 164.93 ***
International RetailService (IRS)

UnitedKingdom

0.0009 **
0.222 *** 0.019 0.074 *** 0.172 *** −0.018 *** 0.0000 *** 0.276 *** 0.513 *** 300.09 ***
New York StockExchange

United States
0.0010 *** 0.130 *** 0.249 *** 0.041 0.151 *** −0.018 *** 0.0000 *** 0.308 *** 0.461 *** 214.50 ***
NASD OTC BulletinBoard

United States
0.0011 *** 0.011 0.011 0.016 0.172 *** −0.018 *** 0.0000 *** 0.278 *** 0.541 *** 145.57 ***
American StockExchange

United States
0.0009 ** 0.129 *** 0.072 *** 0.065 ** 0.158 *** −0.018 *** 0.0000 *** 0.299 *** 0.506 *** 187.04 ***
Nasdaq
United States 0.0008 ** 0.071 *** 0.124 *** −0.009 0.150 *** −0.019 *** 0.0000 * 0.242 *** 0.671 *** 218.21 ***
In the estimation value weighted market return (MRt) and value weighted daily symmetrical percentage change in market percentage spread (MLt) are used.MRother refers to the value weighted market return of the foreign market against which BSE's relationship is tested. Chi2 tests the significance of all coefficientscombined. Data for the study is provided by Dicle (2008) based on Reuters through “QuoteCenter” application of Equis International for the time period startingwith January, 2000 until the end of 2007. Statistical significance levels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.

Table 4
Results for market-wide relationship between Indian National Exchange (INE) returns and international equity markets' returns using Eq. (1).
Dependent: Indian National Exchange


Exchange
Country Constant MRother,t MRother,t−1 MRother,t−2 MRt−1 MLt−1 ArchCons.
Arch
Garch Chi2

Australia
0.0010 *** 0.527 *** −0.076 ** 0.012 0.224 *** −0.009 *** 0.0000 *** 0.276 *** 0.568 *** 319.26 ***
Vienna Stock Exchange
Austria 0.0012 *** 0.119 *** 0.041 0.016 0.219 *** −0.009 *** 0.0001 *** 0.335 *** 0.390 *** 125.73 *** Euronext Brussels Belgium 0.0008 * 0.126 *** 0.015 0.012 0.159 *** −0.009 *** 0.0000 *** 0.314 *** 0.496 *** 83.75 *** TSX Venture Exchange Canada 0.0006 0.156 *** 0.112 *** 0.012 0.225 *** −0.009 *** 0.0001 *** 0.284 *** 0.340 *** 199.05 *** Toronto Stock Exchange Canada 0.0012 *** 0.255 *** 0.223 *** 0.016 0.207 *** −0.008 *** 0.0001 *** 0.313 *** 0.400 *** 295.00 *** Santiago StockExchange
Chile
0.0012 *** 0.180 *** 0.221 *** −0.060 0.300 *** −0.009 *** 0.0001 *** 0.299 *** 0.268 * 150.91 ***
Shanghai StockExchange

China
0.0013 *** −0.002 0.004 0.035 0.235 *** −0.009 *** 0.0001 *** 0.302 *** 0.377 *** 109.46 ***
Shenzhen StockExchange

China
0.0013 *** −0.012 −0.001 0.031 0.232 *** −0.009 *** 0.0001 *** 0.299 *** 0.388 *** 108.68 ***
Copenhagen StockExchange

Denmark
0.0012 *** 0.260 *** 0.041 0.012 0.198 *** −0.009 *** 0.0000 *** 0.318 *** 0.437 *** 223.50 ***
Cairo StockExchange

Egypt
0.0019 *** 0.087 *** −0.022 0.016 0.148 *** −0.007 *** 0.0000 0.243 *** 1.042 *** 44.42 ***
Helsinki StockExchange

Finland
0.0011 *** 0.097 *** 0.047 *** −0.014 0.226 *** −0.009 *** 0.0000 *** 0.284 *** 0.463 *** 168.13 ***
Euronext Paris
France 0.0009 ** 0.142 *** 0.025 −0.002 0.144 *** −0.008 *** 0.0000 *** 0.324 *** 0.488 *** 59.05 *** Berlin Stock Exchange Germany 0.0012 *** 0.382 *** 0.103 ** −0.031 0.209 *** −0.009 *** 0.0000 *** 0.278 *** 0.494 *** 239.78 *** Stuttgart StockExchange
Germany
0.0014 *** 0.077 *** −0.023 0.022 0.335 *** −0.010 *** 0.0001 ** 0.271 *** 0.342 * 189.50 ***
Munich StockExchange

Germany
0.0012 *** 0.293 *** 0.062 −0.018 0.218 *** −0.009 *** 0.0000 *** 0.271 *** 0.486 *** 209.45 ***
Frankfurt StockExchange

Germany
0.0014 *** 0.233 *** −0.002 −0.051 * 0.222 *** −0.009 *** 0.0000 *** 0.303 *** 0.444 *** 196.75***
XETRA
Germany 0.0014 *** 0.067 *** 0.045 *** −0.008 0.231 *** −0.009 *** 0.0001 *** 0.324 *** 0.373 *** 149.65 *** Duesseldorf StockExchange
Germany
0.0013 *** 0.207 *** 0.059 *** 0.000 0.208 *** −0.009 *** 0.0000 *** 0.274 *** 0.506 *** 242.82 ***
Hamburg StockExchange

Germany
0.0012 *** 0.240 *** 0.118 *** −0.030 0.211 *** −0.009 *** 0.0000 *** 0.301 *** 0.452 *** 194.75 ***
Athens StockExchange

Greece
0.0014 *** 0.174 *** −0.030 0.047 ** 0.260 *** −0.009 *** 0.0001 *** 0.370 *** 0.178 ** 212.16 ***
Hong Kong StockExchange

Hong Kong
0.0013 *** 0.378 *** −0.076 *** −0.008 0.208 *** −0.010 *** 0.0001 *** 0.278 *** 0.384 *** 367.64 ***
Jakarta StockExchange

Indonesia
0.0008 ** 0.178 *** −0.074 *** −0.011 0.252 *** −0.009 *** 0.0001 *** 0.291 *** 0.397 *** 213.52 ***
Milano StockExchange

Italy
0.0013 *** 0.233 *** 0.083 *** −0.017 0.212 *** −0.009 *** 0.0001 *** 0.291 *** 0.417 *** 219.45 ***
Tokyo StockExchange

Japan
0.0015 *** 0.204 *** 0.035 0.012 0.200 *** −0.008 *** 0.0001 *** 0.319 *** 0.363 *** 194.90 ***
Osaka Stock Exchange
Japan 0.0013 *** 0.260 *** 0.017 0.002 0.225 *** −0.008 *** 0.0000 *** 0.291 *** 0.501 *** 197.43 *** Japan SDAQS Japan 0.0014 *** 0.181 *** −0.085 *** 0.008 0.201 *** −0.009 *** 0.0000 *** 0.285 *** 0.485 *** 158.80 *** NASDAQ Japan Japan 0.0014 *** 0.081 *** −0.031 ** 0.001 0.265 *** −0.008 *** 0.0001 *** 0.278 *** 0.382 *** 221.52 *** Amman FinancialMarket
Jordan
0.0011 *** 0.088 * 0.059 0.087 ** 0.171 *** −0.007 *** 0.0000 ** 0.243 *** 0.520 *** 89.82 ***
Korea Stock Exchange
Korea 0.0015 *** 0.239 *** −0.026 −0.028 * 0.206 *** −0.009 *** 0.0001 *** 0.254 *** 0.411 *** 327.38 *** KOSDAQ Korea 0.0017 *** 0.183 *** −0.041 *** −0.027 * 0.197 *** −0.009 *** 0.0001 *** 0.245 *** 0.430 *** 249.12 *** Kuala LumpurStock Exchange
Malaysia
0.0010 *** 0.235 *** −0.034 −0.018 0.259 *** −0.009 *** 0.0000 *** 0.282 *** 0.515 *** 256.65 ***
EuronextAmsterdam

Netherlands
0.0011 ** 0.139 *** 0.035 −0.016 0.132 *** −0.010 *** 0.0000 *** 0.296 *** 0.519 *** 84.59 ***
New Zealand StockExchange

NewZealand

0.0012 ***
0.223 *** −0.067 * 0.089 ** 0.224 *** −0.009 *** 0.0000 *** 0.301 *** 0.530 *** 199.89 ***
Oslo Stock Exchange
Norway 0.0013 *** 0.025 ** 0.024 ** −0.015 0.229 *** −0.009 *** 0.0001 *** 0.284 *** 0.448 *** 128.94 *** Lima Stock Exchange Peru 0.0010 *** 0.081 ** 0.021 0.017 0.217 *** −0.008 *** 0.0001 *** 0.308 *** 0.445 *** 94.24 *** Stock Exchange ofSingapore
Singapore
0.0009 ** 0.464 *** 0.012 −0.008 0.223 *** −0.008 *** 0.0000 *** 0.238 *** 0.607 *** 455.59 ***
JohannesburgStock Exchange

South Africa
0.0012 *** 0.350 *** 0.085 *** 0.047 * 0.183 *** −0.008 *** 0.0001 *** 0.358 *** 0.309 *** 350.71 ***
Barcelona StockExchange

Spain
0.0013 *** 0.226 *** 0.082 *** −0.002 0.219 *** −0.009 *** 0.0000 *** 0.312 *** 0.454 *** 223.23 ***
Stockholm StockExchange

Sweden
0.0013 *** 0.193 *** 0.100 *** 0.029 0.171 *** −0.009 *** 0.0000 *** 0.282 *** 0.529 *** 214.07 ***
CHE SWX SwissExchange

Switzerland
0.0013 *** 0.277 *** 0.024 0.012 0.219 *** −0.009 *** 0.0001 *** 0.324 *** 0.369 *** 231.73 ***


(continued)


Table 4 (continued)

Exchange
Country Constant MRother,t MRother,t−1 MRother,t−2 MRt−1 MLt−1 ArchCons.
Arch
Garch Chi2
Taiwan StockExchange

Taiwan
0.0012 *** 0.189 *** −0.053 ** −0.022 0.220 *** −0.009 *** 0.0000 ** 0.239 *** 0.584 *** 200.56 ***
Stock Exchange ofThailand (SET)

Thailand
0.0011 *** 0.175 *** 0.029 0.003 0.240 *** −0.008 *** 0.0001 *** 0.309 *** 0.382 *** 239.78 ***
SETS (ElectronicTrading Service)

UnitedKingdom

0.0010 ***
0.498 *** 0.111 *** 0.021 0.190 *** −0.008 *** 0.0000 *** 0.330 *** 0.443 *** 423.23 ***
LSE-AIM
UnitedKingdom
0.0012 ***
0.088 *** 0.011 0.002 0.226 *** −0.009 *** 0.0001 *** 0.292 *** 0.369 *** 128.65 ***
International RetailService (IRS)

UnitedKingdom

0.0012 ***
0.195 *** 0.036 0.053 ** 0.225 *** −0.009 *** 0.0000 *** 0.294 *** 0.436 *** 223.66 ***
New York StockExchange

UnitedStates

0.0013 ***
0.119 *** 0.228 *** 0.026 0.231 *** −0.009 *** 0.0001 *** 0.296 *** 0.427 *** 218.05 ***
NASD OTC BulletinBoard

UnitedStates

0.0014 ***
0.013 0.010 0.016 0.244 *** −0.010 *** 0.0000 *** 0.286 *** 0.469 *** 135.56 ***
American StockExchange

UnitedStates

0.0012 ***
0.122 *** 0.097 *** 0.069 *** 0.231 *** −0.009 *** 0.0001 *** 0.314 *** 0.390 *** 215.78 ***
Nasdaq
UnitedStates
0.0012 ***
0.063 *** 0.115 *** −0.024 0.216 *** −0.010 *** 0.0000 ** 0.261 *** 0.610 *** 211.93 ***
In the estimation value weighted market return (MRt) and value weighted daily symmetrical percentage change in market percentage spread (MLt) are used.MRother refers to the value weighted market return of the foreign market against which INE's relationship is tested. Chi2 tests the significance of all coefficientscombined. Data for the study is provided by Dicle (2008) based on Reuters through “QuoteCenter” application of Equis International for the time period startingwith January, 2000 until the end of 2007. Statistical significance levels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.

Table 5Results for market-wide relationship between for both Indian equity markets (Bombay Stock Exchange (BSE) and Indian National Exchange (INE) returns andinternational equity markets' returns using Eq. (1) for two different time periods; 2000–2003 and 2004–2007.

IndianMarket

USMarket

Period C
onstant MRother,t MRother,t−1 MRother,t−2 M RISE,t−1 MLISE,t A rch Cons. A rch Garch C hi2
BombayStockExchange

New YorkStockExchange

2000–2003

0
.0008 0.0703 0.1693 *** 0.0097 0 .1648 *** −0.0115 *** 0.00004 *** 0 .3027 *** 0.5440 *** 62.686 ***
2004–2007

0
.0007 0.2871 *** 0.4156 *** 0.0960 0 .1824 *** −0.0315 *** − 0.00001 0 .2660 *** 0.8237 *** 2 26.255 ***
NASDOTCBulletinBoard

2000–2003

0
.0007 0.0091 0.0170 0.0114 0 .1774 *** −0.0110 *** 0.00004 *** 0 .2796 *** 0.5695 *** 50.409 ***
2004–2007

0
.0013 ** 0.0245 −0.0043 0.0266 0 .1951 *** −0.0342 *** 0.00001 0 .2923 *** 0.7018 ** 1 33.311 ***
AmericanStockExchange

2000–2003

0
.0007 0.0524 0.0190 0.0646 * 0 .1742 *** −0.0112 *** 0.00004 *** 0 .2854 *** 0.5608 *** 49.972 ***
2004–2007

0
.0008 0.2880 *** 0.2300 *** 0.0226 0 .1081 *** −0.0280 *** 0.00009 ** 0 .3004 *** 0.0675 1 85.092 ***
Nasdaq
2000–2003
0
.0005 0.0489 *** 0.0956 *** −0.0151 0 .1569 *** −0.0123 *** 0.00002 * 0 .2469 *** 0.6710 *** 81.801 ***
2004–2007

0
.0010 * 0.2194 *** 0.3165 *** −0.0267 0 .0962 *** −0.0287 *** 0.00012 *** 0 .3537 *** −0.1390 1 93.473 ***
IndianNationalExchange

New YorkStockExchange

2000–2003

0
.0009 ** 0.0697 * 0.1482 *** −0.0199 0 .1657 *** −0.0097 *** 0.00003 ** 0 .2843 *** 0.5842 *** 62.598 ***
2004–2007

0
.0011 ** 0.2618 *** 0.4571 *** 0.2142 *** 0 .3173 *** −0.0099 *** 0.00012 *** 0 .3568 *** −0.1210 * 3 03.462 ***
NASDOTCBulletinBoard

2000–2003

0
.0009 ** 0.0085 0.0095 0.0133 0 .1749 *** −0.0105 *** 0.00003 ** 0 .2750 *** 0.5921 *** 48.713 ***
2004–2007

0
.0018 *** 0.0144 0.0139 0.0210 0 .3594 *** −0.0109 *** 0.00012 *** 0 .3265 *** 0.0074 1 56.684 ***
AmericanStockExchange

2000–2003

0
.0009 * 0.0552 * 0.0131 0.0626 ** 0 .1715 *** -0.0099 *** 0.00003 *** 0 .2936 *** 0.5543 *** 53.510 ***
2004–2007

0
.0011 ** 0.2489 *** 0.3057 *** 0.0724 * 0 .2742 *** −0.0081 *** 0.00011 *** 0 .3813 *** −0.0861 2 81.114 ***
Nasdaq
2000–2003
0
.0008 * 0.0462 *** 0.0882 *** −0.0319 0 .1580 *** −0.0107 *** 0.00002 * 0 .2556 *** 0.6600 *** 83.329 ***
2004–2007

0
.0013 ** 0.1796 *** 0.3499 *** 0.0456 0 .3124 *** −0.0090 *** 0.00010 *** 0 .3922 *** −0.0264 3 19.732 ***
In the estimation value weighted market return (MRt) and value weighted daily symmetrical percentage change in market percentage spread (MLt) are used.MRother refers to the value weighted market return of the foreign market against which INE's relationship is tested. Chi2 tests the significance of all coefficientscombined. Data for the study is provided by Dicle (2008) based on Reuters through “QuoteCenter” application of Equis International for the time period startingwith January, 2000 until the end of 2007. Statistical significance levels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.

Table 6Results for market-wide relationship, with daily dummy variables, between Bombay Stock Exchange (BSE) returns and international equity markets' returns usingEq. (2).


Exchange Country Constant Monday Tuesday Thursday Friday MRother,t


Australia 0.0015 ** −0.0021 −0.0027 ** −0.0003 −0.0012 0.5301 ***

Vienna Stock Exchange Austria 0.0009 −0.0010 −0.0010 0.0004 −0.0004 0.0696 *Euronext Brussels Belgium 0.0014 −0.0009 −0.0015 −0.0008 −0.0017 0.1321 ***TSX Venture Exchange Canada 0.0012 * −0.0025 * −0.0011 −0.0005 −0.0027 *** 0.2045 ***Toronto Stock Exchange Canada 0.0011 −0.0020 −0.0011 0.0005 −0.0008 0.2787 ***Santiago Stock Exchange Chile 0.0000 −0.0009 −0.0006 0.0017 0.0007 0.2574 ***Shanghai Stock Exchange China 0.0018 ** −0.0021 −0.0028 * 0.0001 −0.0008 0.0585 **Shenzhen Stock Exchange China 0.0018 ** −0.0021 −0.0029 * 0.0002 −0.0008 0.0443 *Copenhagen StockExchange

Denmark 0.0010 −0.0006 −0.0006 0.0009 −0.0005 0.3078 ***

Cairo Stock Exchange Egypt 0.0008 −0.0069 0.0000 0.0009 −0.0011 0.0948 ***Helsinki Stock Exchange Finland 0.0012 * −0.0015 −0.0010 −0.0001 −0.0008 0.1115 ***Euronext Paris France 0.0019 * −0.0013 −0.0024 −0.0007 −0.0014 0.1547 ***Berlin Stock Exchange Germany 0.0013 * −0.0017 −0.0014 0.0003 −0.0007 0.4041 ***Stuttgart Stock Exchange Germany 0.0009 −0.0021 −0.0024 0.0014 0.0001 0.0898 ***Munich Stock Exchange Germany 0.0013 * −0.0019 −0.0015 0.0002 −0.0006 0.3341 ***Frankfurt Stock Exchange Germany 0.0015 ** −0.0015 −0.0013 0.0003 −0.0006 0.2062 ***XETRA Germany 0.0013 * −0.0012 −0.0009 0.0006 −0.0005 0.0847 ***Duesseldorf StockExchange

Germany 0.0016 ** −0.0019 −0.0017 −0.0001 −0.0008 0.2527 ***

Hamburg Stock Exchange Germany 0.0011 −0.0014 −0.0014 0.0005 −0.0004 0.2167 ***Athens Stock Exchange Greece 0.0013 * −0.0004 −0.0003 0.0004 −0.0006 0.1956 ***Hong Kong StockExchange

Hong Kong 0.0012 −0.0009 −0.0014 0.0003 −0.0007 0.3978 ***

Jakarta Stock Exchange Indonesia 0.0008 −0.0009 −0.0018 0.0007 −0.0010 0.1889 ***Milano Stock Exchange Italy 0.0010 −0.0013 −0.0009 0.0006 −0.0003 0.2455 ***Tokyo Stock Exchange Japan 0.0013 * −0.0009 −0.0012 0.0008 −0.0007 0.1902 ***Osaka Stock Exchange Japan 0.0012 * −0.0022 −0.0019 0.0005 −0.0007 0.2483 ***Japan SDAQS Japan 0.0012 −0.0004 −0.0012 0.0005 −0.0011 0.2190 ***NASDAQ Japan Japan 0.0010 −0.0014 −0.0018 0.0010 −0.0001 0.0751 ***Amman Financial Market Jordan 0.0013 ** −0.0028 * −0.0012 0.0005 −0.0006 0.1425 ***Korea Stock Exchange Korea 0.0017 ** −0.0023 * −0.0018 0.0001 −0.0009 0.2447 ***KOSDAQ Korea 0.0018 ** −0.0037 *** −0.0006 0.0001 −0.0010 0.1826 ***Kuala Lumpur StockExchange

Malaysia 0.0007 −0.0013 −0.0011 0.0006 −0.0002 0.2435 ***

Euronext Amsterdam Netherlands 0.0016 −0.0012 −0.0014 −0.0008 −0.0019 0.1789 ***New Zealand StockExchange

New Zealand 0.0011 −0.0010 −0.0018 0.0003 −0.0004 0.2527 ***

Oslo Stock Exchange Norway 0.0011 −0.0012 −0.0009 0.0006 −0.0006 0.0256 **Lima Stock Exchange Peru 0.0008 −0.0024 * −0.0009 0.0006 −0.0006 0.0875 **Stock Exchange ofSingapore

Singapore 0.0010 −0.0011 −0.0020 0.0003 −0.0012 0.4836 ***

Johannesburg StockExchange

South Africa 0.0012 * −0.0021 * −0.0012 0.0006 −0.0002 0.3439 ***

Barcelona Stock Exchange Spain 0.0015 ** −0.0017 −0.0019 0.0004 −0.0008 0.2441 ***Stockholm StockExchange

Sweden 0.0014 ** −0.0011 −0.0012 0.0001 −0.0009 0.2323 ***

CHE SWX Swiss Exchange Switzerland 0.0014 ** −0.0011 −0.0016 0.0003 −0.0008 0.3010 ***Taiwan Stock Exchange Taiwan 0.0011 −0.0011 −0.0007 0.0001 −0.0003 0.2121 ***Stock Exchange ofThailand (SET)

Thailand 0.0004 −0.0010 −0.0006 0.0011 −0.0002 0.1996 ***

SETS (Electronic TradingService)

United Kingdom 0.0014 * −0.0020 * −0.0013 0.0002 −0.0013 0.5272 ***

LSE-AIM United Kingdom 0.0017 ** −0.0024 * −0.0017 −0.0001 −0.0013 0.1278 ***International RetailService (IRS)

United Kingdom 0.0016 ** −0.0032 ** −0.0019 −0.0001 −0.0008 0.2173 ***

New York Stock Exchange United States 0.0015 ** −0.0030 ** −0.0018 0.0005 −0.0005 0.1261 ***NASD OTC Bulletin Board United States 0.0017 ** −0.0035 ** −0.0020 0.0004 −0.0007 0.0094American Stock Exchange United States 0.0016 ** −0.0030 ** −0.0015 0.0004 −0.0011 0.1260 ***Nasdaq United States 0.0013 * −0.0028 ** −0.0012 0.0002 −0.0005 0.0691 ***

In the estimation value weighted market return (MRt) and value weighted daily symmetrical percentage change in market percentage spread (MLt) are used(MRother) refers to the value weighted market return of the foreign market against which BSE's relationship is tested. Daily (D) dummy variables are assignedvalues of one for Mondays, Tuesdays, Thursdays, and for Fridays. Chi2 tests the significance of all coefficients combined. Data for the study is provided by Dicle(2008) based on Reuters through “QuoteCenter” application of Equis International for the time period starting with January, 2000 until the end of 2007Statistical significance levels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.


.

.


MRother,t−1 MRother,t−2 MRt−1 MLt Arch Cons. Arch Garch Chi2

−0.0700 * 0.0082 0.1630 *** −0.0186 *** 0.0000 *** 0.2413 *** 0.6306 *** 305.16 ***

0.0250 0.0615 0.1790 *** −0.0190 *** 0.0000 *** 0.2819 *** 0.5576 *** 173.39 ***−0.0023 0.0267 0.1715 *** −0.0146 *** 0.0000 *** 0.2602 *** 0.6135 *** 142.15 ***

0.1063 *** −0.0032 0.1604 *** −0.0185 *** 0.0001 *** 0.3011 *** 0.3711 *** 241.27 ***0.1951 *** 0.0040 0.1486 *** −0.0183 *** 0.0000 *** 0.2870 *** 0.5149 *** 314.10 ***0.1617 ** −0.0164 0.1979 *** −0.0258 *** 0.0000 ** 0.2968 *** 0.3959 ** 231.75 ***

−0.0092 0.0418 0.1455 *** −0.0183 *** 0.0001 *** 0.3508 *** 0.3765 *** 152.45 ***−0.0167 0.0238 0.1438 *** −0.0182 *** 0.0001 *** 0.3487 *** 0.3798 *** 148.14 ***

0.0264 0.0096 0.1475 *** −0.0175 *** 0.0001 *** 0.3755 *** 0.3049 *** 350.76 ***

−0.0433 0.0247 0.0604 ** −0.0275 *** 0.0000 0.2783 *** 0.6532 *** 109.68 ***0.0471 *** 0.0025 0.1626 *** −0.0185 *** 0.0000 *** 0.2629 *** 0.5691 *** 236.42 ***0.0515 0.0025 0.1500 *** −0.0111 *** 0.0000 *** 0.3516 *** 0.4633 *** 81.94 ***0.0965 ** −0.0007 0.1410 *** −0.0182 *** 0.0000 *** 0.2879 *** 0.5061 *** 296.86 ***0.0209 0.0095 0.1739 *** −0.0259 *** 0.0000 0.2455 *** 0.6053 *** 176.03 ***0.0598 * 0.0149 0.1505 *** −0.0184 *** 0.0000 *** 0.2701 *** 0.5417 *** 266.93 ***0.0387 −0.0566 * 0.1490 *** −0.0186 *** 0.0000 *** 0.2868 *** 0.4784 *** 209.54 ***0.0327 * −0.0177 0.1764 *** −0.0184 *** 0.0001 *** 0.3217 *** 0.4137 *** 212.78 ***0.0652 *** 0.0283 0.1480 *** −0.0180 *** 0.0000 *** 0.2845 *** 0.5451 *** 328.68 ***

0.1404 *** −0.0238 0.1477 *** −0.0180 *** 0.0000 *** 0.2958 *** 0.5192 *** 256.62 ***−0.0011 0.0481 ** 0.1669 *** −0.0184 *** 0.0001 *** 0.3643 *** 0.1725 ** 330.27 ***−0.0847 *** 0.0500 ** 0.1396 *** −0.0179 *** 0.0000 ** 0.2252 *** 0.6348 *** 461.23 ***

−0.0818 *** −0.0328 0.1813 *** −0.0184 *** 0.0000 *** 0.2721 *** 0.5318 *** 262.66 ***0.1122 *** −0.0113 0.1582 *** −0.0193 *** 0.0001 *** 0.2893 *** 0.4312 *** 266.63 ***0.0486 ** 0.0265 0.1366 *** −0.0194 *** 0.0001 *** 0.3250 *** 0.3399 *** 221.54 ***0.0149 0.0164 0.1668 *** −0.0221 *** 0.0001 *** 0.2768 *** 0.3975 *** 249.70 ***

−0.0712 *** 0.0430 * 0.1391 *** −0.0195 *** 0.0000 *** 0.2337 *** 0.5736 *** 192.98 ***−0.0008 0.0012 0.1774 *** −0.0233 *** 0.0001 *** 0.2500 *** 0.2425 224.01 ***

0.0085 0.0752 * 0.0499 ** −0.0216 *** 0.0000 *** 0.3152 *** 0.3846 *** 128.94 ***−0.0352 * −0.0144 0.1237 *** −0.0177 *** 0.0000 *** 0.2171 *** 0.5380 *** 353.02 ***−0.0388 ** −0.0191 0.1378 *** −0.0196 *** 0.0000 ** 0.1769 *** 0.5911 *** 304.75 ***−0.0433 −0.0116 0.1639 *** −0.0192 *** 0.0000 ** 0.2481 *** 0.6539 *** 222.15 ***

0.0282 −0.0096 0.1567 *** −0.0163 *** 0.0000 ** 0.2447 *** 0.6414 *** 160.37 ***−0.0313 0.1021 ** 0.1604 *** −0.0173 *** 0.0000 *** 0.2946 *** 0.5451 *** 227.36 ***

0.0117 −0.0133 0.1742 *** −0.0184 *** 0.0000 *** 0.2565 *** 0.5550 *** 160.03 ***0.0721 * 0.0105 0.1670 *** −0.0197 *** 0.0000 *** 0.2861 *** 0.5634 *** 193.98 ***0.0181 0.0018 0.1462 *** −0.0170 *** 0.0000 *** 0.2325 *** 0.5732 *** 464.17 ***

0.0679 ** 0.0852 *** 0.1261 *** −0.0161 *** 0.0000 *** 0.3454 *** 0.4341 *** 417.14 ***

0.0760 *** 0.0054 0.1504 *** −0.0179 *** 0.0000 *** 0.3377 *** 0.4648 *** 296.61 ***0.0945 *** 0.0316 0.1319 *** −0.0176 *** 0.0000 *** 0.2724 *** 0.5584 *** 279.53 ***

0.0251 0.0356 0.1509 *** −0.0176 *** 0.0000 *** 0.3047 *** 0.5188 *** 323.85 ***−0.0553 ** −0.0078 0.1634 *** −0.0193 *** 0.0000 ** 0.2472 *** 0.6442 *** 232.10 ***−0.0086 −0.0227 0.1881 *** −0.0188 *** 0.0000 ** 0.2451 *** 0.6582 *** 233.70 ***

0.0962 ** 0.0271 0.1460 *** −0.0166 *** 0.0000 *** 0.3057 *** 0.4839 *** 454.50 ***

0.0409 * −0.0029 0.1627 *** −0.0187 *** 0.0000 *** 0.2805 *** 0.5141 *** 174.40 ***0.0234 0.0785 *** 0.1682 *** −0.0181 *** 0.0000 *** 0.2836 *** 0.4933 *** 309.50 ***

0.2464 *** 0.0413 0.1457 *** −0.0184 *** 0.0000 *** 0.3266 *** 0.4525 *** 226.24 ***0.0126 0.0169 0.1674 *** −0.0186 *** 0.0000 *** 0.2917 *** 0.5396 *** 153.35 ***0.0693 *** 0.0670 *** 0.1544 *** −0.0181 *** 0.0000 *** 0.3133 *** 0.4920 *** 203.62 ***0.1200 *** −0.0071 0.1449 *** −0.0194 *** 0.0000 * 0.2475 *** 0.6668 *** 225.33 ***


Table 7Results for market-wide relationship, with daily dummy variables, between Indian National Exchange (INE) returns and international equity markets' returnsusing Eq. (2).


Exchange Country Constant Monday Tuesday Thursday Friday MRother,t

National Automated Trading Australia 0.0028 *** −0.0037 *** −0.0034 *** −0.0015 * −0.0024 *** 0.5351 ***Vienna Stock Exchange Austria 0.0022 *** −0.0025 * −0.0015 −0.0007 −0.0015 0.1170 ***Euronext Brussels Belgium 0.0029 *** −0.0028 * −0.0024 −0.0025 ** −0.0030 ** 0.1304 ***TSX Venture Exchange Canada 0.0023 *** −0.0042 *** −0.0014 −0.0013 −0.0036 *** 0.1760 ***Toronto Stock Exchange Canada 0.0024 *** −0.0034 *** −0.0018 −0.0006 −0.0019 ** 0.2503 ***Santiago Stock Exchange Chile 0.0011 −0.0023 −0.0022 0.0009 0.0002 0.1688 ***Shanghai Stock Exchange China 0.0027 *** −0.0037 *** −0.0028 * −0.0008 −0.0021 ** −0.0054Shenzhen Stock Exchange China 0.0028 *** −0.0037 *** −0.0029 * −0.0008 −0.0022 ** −0.0141Copenhagen Stock Exchange Denmark 0.0021 *** −0.0027 ** −0.0010 −0.0004 −0.0018 * 0.2586 ***Cairo Stock Exchange Egypt 0.0023 *** −0.0092 −0.0012 −0.0001 −0.0016 0.0855 ***Helsinki Stock Exchange Finland 0.0024 *** −0.0032 ** −0.0014 −0.0010 −0.0021 ** 0.0958 ***Euronext Paris France 0.0030 *** −0.0033 ** −0.0027 −0.0022 * −0.0026 * 0.1467 ***Berlin Stock Exchange Germany 0.0025 *** −0.0036 *** −0.0016 −0.0009 −0.0020 ** 0.3802 ***Stuttgart Stock Exchange Germany 0.0014 * −0.0036 ** −0.0027 0.0010 0.0003 0.0833 ***Munich Stock Exchange Germany 0.0025 *** −0.0036 *** −0.0018 −0.0008 −0.0019 ** 0.2922 ***Frankfurt Stock Exchange Germany 0.0027 *** −0.0033 *** −0.0016 −0.0009 −0.0019 ** 0.2297 ***XETRA Germany 0.0024 *** −0.0029 ** −0.0012 −0.0004 −0.0016 * 0.0624 ***Duesseldorf Stock Exchange Germany 0.0028 *** −0.0036 *** −0.0023 * −0.0011 −0.0020 ** 0.2077 ***Hamburg Stock Exchange Germany 0.0023 *** −0.0034 *** −0.0016 −0.0006 −0.0016 * 0.2368 ***Athens Stock Exchange Greece 0.0024 *** −0.0016 −0.0015 −0.0008 −0.0017 * 0.1715 ***Hong Kong Stock Exchange Hong Kong 0.0021 *** −0.0022 * −0.0015 −0.0001 −0.0014 0.3749 ***Jakarta Stock Exchange Indonesia 0.0016 ** −0.0019 −0.0013 0.0001 −0.0017 * 0.1767 ***Milano Stock Exchange Italy 0.0024 *** −0.0032 ** −0.0015 −0.0007 −0.0015 * 0.2275 ***Tokyo Stock Exchange Japan 0.0026 *** −0.0025 * −0.0016 −0.0006 −0.0020 ** 0.2052 ***Osaka Stock Exchange Japan 0.0024 *** −0.0025 * −0.0016 −0.0009 −0.0017 * 0.2630 ***Japan SDAQS Japan 0.0025 *** −0.0018 −0.0017 −0.0007 −0.0022 ** 0.1822 ***NASDAQ Japan Japan 0.0020 ** −0.0021 −0.0016 0.0000 −0.0009 0.0776 ***Amman Financial Market Jordan 0.0018 *** −0.0033 ** −0.0017 * −0.0001 −0.0007 0.0914 **Korea Stock Exchange Korea 0.0026 *** −0.0033 ** −0.0020 −0.0005 −0.0018 * 0.2380 ***KOSDAQ Korea 0.0029 *** −0.0040 *** −0.0007 −0.0008 −0.0018 * 0.1847 ***Kuala Lumpur Stock Exchange Malaysia 0.0018 *** −0.0023 * −0.0018 −0.0004 −0.0013 0.2315 ***Euronext Amsterdam Netherlands 0.0033 *** −0.0032 ** −0.0025 * −0.0025 ** −0.0032 *** 0.1440 ***New Zealand Stock Exchange New Zealand 0.0023 *** −0.0027 ** −0.0023 * −0.0005 −0.0014 0.2214 ***Oslo Stock Exchange Norway 0.0022 *** −0.0028 ** −0.0011 −0.0003 −0.0016 * 0.0244 **Lima Stock Exchange Peru 0.0019 *** −0.0038 *** −0.0012 −0.0001 −0.0014 0.0825 **Stock Exchange of Singapore Singapore 0.0021 *** −0.0023 * −0.0023 * −0.0007 −0.0019 ** 0.4616 ***Johannesburg Stock Exchange South Africa 0.0024 *** −0.0039 *** −0.0012 −0.0007 −0.0017 * 0.3563 ***Barcelona Stock Exchange Spain 0.0027 *** −0.0035 *** −0.0023 * −0.0008 −0.0018 ** 0.2283 ***Stockholm Stock Exchange Sweden 0.0025 *** −0.0027 ** −0.0017 −0.0009 −0.0020 ** 0.1917 ***CHE SWX Swiss Exchange Switzerland 0.0025 *** −0.0028 ** −0.0018 −0.0008 −0.0020 ** 0.2761 ***Taiwan Stock Exchange Taiwan 0.0021 *** −0.0025 ** −0.0016 −0.0006 −0.0012 0.1833 ***Stock Exchange of Thailand (SET) Thailand 0.0021 *** −0.0027 ** −0.0019 −0.0005 −0.0015 0.1740 ***SETS (Electronic Trading Service) United Kingdom 0.0026 *** −0.0039 *** −0.0017 −0.0013 −0.0024 *** 0.4990 ***LSE-AIM United Kingdom 0.0026 *** −0.0038 *** −0.0019 −0.0010 −0.0020 ** 0.0885 ***International Retail Service (IRS) United Kingdom 0.0025 *** −0.0040 *** −0.0021 −0.0011 −0.0015 0.1914 ***New York Stock Exchange United States 0.0028 *** −0.0048 *** −0.0025 * −0.0009 −0.0018 * 0.1123 ***NASD OTC Bulletin Board United States 0.0029 *** −0.0052 *** −0.0028 ** −0.0007 −0.0019 ** 0.0101American Stock Exchange United States 0.0027 *** −0.0046 *** −0.0017 −0.0008 −0.0023 ** 0.1192 ***Nasdaq United States 0.0027 *** −0.0044 *** −0.0024 * −0.0010 −0.0018 * 0.0593 ***

In the estimation value weighted market return (MRt) and value weighted daily symmetrical percentage change in market percentage spread (MLt) are used.MRother refers to the value weighted market return of the foreign market against which INE's relationship is tested. Daily (D) dummy variables are assigned valuesof one for Mondays, Tuesdays, Thursdays, and for Fridays. Chi2 tests the significance of all coefficients combined. Data for the study is provided by Dicle (2008)based on Reuters through “QuoteCenter” application of Equis International for the time period starting with January, 2000 until the end of 2007. Statisticalsignificance levels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.


Dependent: Indian National

MRother,t−1 MRother,t−2 MRt−1 MLt Arch Cons. Arc Garch Chi2

−0.0731 ** 0.0036 0.2186 *** −0.0084 *** 0.0000 *** 0.2872 *** 0.5845 *** 333.87 ***0.0326 0.0221 0.2150 *** −0.0088 *** 0.0000 *** 0.3339 *** 0.4161 *** 128.28 ***0.0237 0.0148 0.1618 *** −0.0089 *** 0.0000 *** 0.3196 *** 0.4801 *** 96.64 ***0.1412 *** 0.0124 0.2167 *** −0.0089 *** 0.0001 *** 0.2931 *** 0.3410 *** 225.53 ***0.2249 *** 0.0224 0.1943 *** −0.0086 *** 0.0000 *** 0.3162 *** 0.4205 *** 305.34 ***0.2148 *** −0.0545 0.2981 *** −0.0089 *** 0.0001 *** 0.3065 *** 0.2862 * 155.03 ***

−0.0009 0.0312 0.2231 *** −0.0090 *** 0.0001 *** 0.3061 *** 0.4070 *** 117.72 ***−0.0058 0.0265 0.2203 *** −0.0090 *** 0.0001 *** 0.3036 *** 0.4175 *** 118.07 ***

0.0438 * 0.0176 0.1911 *** −0.0091 *** 0.0000 *** 0.3229 *** 0.4394 *** 228.49 ***−0.0300 0.0232 0.1501 *** −0.0073 *** 0.0000 0.2445 *** 1.0498 *** 49.06 ***

0.0491 *** −0.0118 0.2218 *** −0.0090 *** 0.0000 *** 0.2896 *** 0.4527 *** 174.28 ***0.0329 −0.0017 0.1457 *** −0.0079 *** 0.0000 *** 0.3291 *** 0.4879 *** 69.39 ***0.1127 ** −0.0323 0.2022 *** −0.0092 *** 0.0000 *** 0.2870 *** 0.4987 *** 247.68 ***

−0.0267 0.0181 0.3067 *** −0.0105 *** 0.0000 0.2514 *** 0.6160 *** 168.91 ***0.0695 * −0.0136 0.2092 *** −0.0092 *** 0.0000 *** 0.2795 *** 0.4927 *** 217.93 ***0.0014 −0.0513 * 0.2146 *** −0.0092 *** 0.0000 *** 0.3120 *** 0.4491 *** 200.67 ***0.0448 *** −0.0050 0.2229 *** −0.0087 *** 0.0001 *** 0.3281 *** 0.3834 *** 154.75 ***0.0610 *** 0.0037 0.1974 *** −0.0091 *** 0.0000 *** 0.2849 *** 0.5161 *** 253.89 ***0.1229 *** −0.0268 0.2010 *** −0.0090 *** 0.0000 *** 0.3046 *** 0.4786 *** 199.94 ***

−0.0276 0.0496 ** 0.2563 *** −0.0084 *** 0.0001 *** 0.3718 *** 0.2015 ** 216.16 ***−0.0763 *** −0.0063 0.2027 *** −0.0097 *** 0.0001 *** 0.2864 *** 0.3906 *** 375.11 ***−0.0751 *** −0.0086 0.2439 *** −0.0095 *** 0.0001 *** 0.2940 *** 0.4080 *** 220.13 ***

0.0869 *** −0.0177 0.2042 *** −0.0086 *** 0.0000 *** 0.2947 *** 0.4380 *** 222.25 ***0.0377 0.0110 0.1937 *** −0.0085 *** 0.0001 *** 0.3300 *** 0.3596 *** 201.79 ***0.0174 0.0014 0.2182 *** −0.0082 *** 0.0000 *** 0.2942 *** 0.5198 *** 200.02 ***

−0.0798 *** 0.0109 0.1978 *** −0.0092 *** 0.0000 *** 0.2943 *** 0.4741 *** 171.71 ***−0.0303 ** 0.0039 0.2603 *** −0.0082 *** 0.0000 *** 0.2728 *** 0.4318 *** 223.81 ***

0.0521 0.0997 ** 0.1679 *** −0.0072 *** 0.0000 * 0.2541 *** 0.5225 *** 106.75 ***−0.0253 −0.0256 0.1953 *** −0.0089 *** 0.0000 *** 0.2646 *** 0.4405 *** 337.85 ***−0.0386 *** −0.0279 * 0.1865 *** −0.0087 *** 0.0001 *** 0.2520 *** 0.4523 *** 274.72 ***−0.0359 −0.0111 0.2517 *** −0.0093 *** 0.0000 *** 0.2903 *** 0.5250 *** 258.58 ***

0.0395 −0.0138 0.1345 *** −0.0097 *** 0.0000 *** 0.3007 *** 0.5147 *** 96.54 ***−0.0666 * 0.0918 ** 0.2168 *** −0.0087 *** 0.0000 *** 0.3165 *** 0.5309 *** 202.05 ***

0.0251 *** −0.0136 0.2226 *** −0.0091 *** 0.0000 *** 0.2904 *** 0.4479 *** 131.98 ***0.0226 0.0111 0.2103 *** −0.0084 *** 0.0000 *** 0.3159 *** 0.4465 *** 99.72 ***0.0108 −0.0038 0.2185 *** −0.0081 *** 0.0000 *** 0.2531 *** 0.5870 *** 464.37 ***0.0855 *** 0.0471 * 0.1782 *** −0.0081 *** 0.0001 *** 0.3687 *** 0.3069 *** 361.21 ***0.0874 *** −0.0068 0.2088 *** −0.0088 *** 0.0000 *** 0.3215 *** 0.4847 *** 225.49 ***0.1015 *** 0.0313 0.1646 *** −0.0085 *** 0.0000 *** 0.2918 *** 0.5302 *** 228.41 ***0.0276 0.0105 0.2079 *** −0.0091 *** 0.0000 *** 0.3271 *** 0.4118 *** 230.71 ***

−0.0500 ** −0.0205 0.2142 *** −0.0090 *** 0.0000 ** 0.2503 *** 0.5804 *** 209.09 ***0.0242 0.0046 0.2355 *** −0.0079 *** 0.0000 *** 0.3043 *** 0.4483 *** 245.65 ***0.1215 *** 0.0238 0.1837 *** −0.0083 *** 0.0000 *** 0.3361 *** 0.4593 *** 435.74 ***0.0149 0.0055 0.2196 *** −0.0091 *** 0.0001 *** 0.2989 *** 0.3987 *** 135.07 ***0.0478 * 0.0528 ** 0.2138 *** −0.0089 *** 0.0000 *** 0.2927 *** 0.4850 *** 238.64 ***0.2228 *** 0.0292 0.2145 *** −0.0096 *** 0.0000 *** 0.3046 *** 0.5020 *** 227.11 ***0.0128 0.0173 0.2284 *** −0.0101 *** 0.0000 *** 0.3008 *** 0.5340 *** 148.14 ***0.0979 *** 0.0711 *** 0.2240 *** −0.0094 *** 0.0001 *** 0.3279 *** 0.4097 *** 226.27 ***0.1101 *** −0.0207 0.2059 *** −0.0100 *** 0.0000 ** 0.2773 *** 0.6193 *** 221.34 ***


Table 8Results for market-wide Granger type causal relationship between for Bombay Stock Exchange (BSE) returns and international equity markets' returns usingEq. (3).


Exchange Country Constant MRother,t−1 MRother,t−2 MRother,t−3 MRt−1

National Automated Trading Australia 0.0010 ** −0.0684 0.0455 −0.0819 0.2175 ***Vienna Stock Exchange Austria 0.0012 *** 0.0657 0.1366 *** −0.0960 ** 0.1750 ***Euronext Brussels Belgium 0.0003 0.0027 0.0461 −0.0018 0.1999 ***TSX Venture Exchange Canada 0.0007 0.0895 ** 0.0430 0.0002 0.1821 ***Toronto Stock Exchange Canada 0.0009 ** 0.2293 *** 0.0522 0.0568 0.1529 ***Santiago Stock Exchange Chile 0.0011 ** 0.2223 *** 0.0245 −0.0311 0.2304 ***Shanghai Stock Exchange China 0.0011 ** −0.0158 0.0565 * −0.0228 0.1802 ***Shenzhen Stock Exchange China 0.0012 *** −0.0198 0.0475 * −0.0354 0.1800 ***Copenhagen Stock Exchange Denmark 0.0011 ** 0.0751 ** 0.0390 0.0427 0.1768 ***Cairo Stock Exchange Egypt 0.0021 *** 0.0478 −0.0467 0.0320 0.2525 ***Helsinki Stock Exchange Finland 0.0009 ** 0.0543 *** 0.0160 −0.0116 0.1807 ***Euronext Paris France 0.0006 0.0697 ** 0.0143 0.0054 0.1926 ***Berlin Stock Exchange Germany 0.0011 *** 0.1193 *** 0.0572 −0.0639 0.1852 ***Stuttgart Stock Exchange Germany 0.0012 ** 0.0237 0.0274 −0.0288 0.2018 ***Munich Stock Exchange Germany 0.0011 *** 0.1019 ** 0.0592 −0.0616 0.1885 ***Frankfurt Stock Exchange Germany 0.0011 ** 0.0745 ** −0.0674 * −0.0154 0.1951 ***XETRA Germany 0.0011 *** 0.0278 0.0051 0.0144 0.2040 ***Duesseldorf Stock Exchange Germany 0.0011 *** 0.0775 *** 0.0419 * −0.0068 0.1846 ***Hamburg Stock Exchange Germany 0.0011 *** 0.1445 *** −0.0353 −0.0408 0.1896 ***Athens Stock Exchange Greece 0.0009 * 0.0091 0.0641 ** −0.0132 0.2169 ***Hong Kong Stock Exchange Hong Kong 0.0006 −0.0801 ** 0.0917 *** −0.0217 0.2351 ***Jakarta Stock Exchange Indonesia 0.0010 ** −0.0539 ** −0.0344 −0.0266 0.2181 ***Milano Stock Exchange Italy 0.0011 *** 0.1390 *** 0.0021 0.0395 0.1797 ***Tokyo Stock Exchange Japan 0.0012 ** 0.0430 0.0640 *** −0.0337 0.1644 ***Osaka Stock Exchange Japan 0.0010 ** 0.0307 0.0499 0.0160 0.2261 ***Japan SDAQS Japan 0.0010 ** −0.0615 ** 0.0744 ** 0.0711 *** 0.1710 ***NASDAQ Japan Japan 0.0012 ** −0.0013 0.0204 0.0225 0.1941 ***Amman Financial Market Jordan 0.0012 ** 0.0704 0.0491 −0.0392 0.1448 ***Korea Stock Exchange Korea 0.0009 ** −0.0143 −0.0009 0.0063 0.1942 ***KOSDAQ Korea 0.0012 *** 0.0061 0.0001 0.0176 0.1876 ***Kuala Lumpur Stock Exchange Malaysia 0.0009 ** 0.0030 −0.0351 −0.0254 0.2212 ***Euronext Amsterdam Netherlands 0.0004 0.0328 −0.0064 −0.0072 0.1931 ***New Zealand Stock Exchange New Zealand 0.0010 ** −0.0579 0.1119 ** 0.0108 0.1827 ***Oslo Stock Exchange Norway 0.0012 *** 0.0051 −0.0045 −0.0210 * 0.1954 ***Lima Stock Exchange Peru 0.0007 0.0764 0.0582 −0.0452 0.1982 ***Stock Exchange of Singapore Singapore 0.0010 ** −0.0147 0.0413 −0.0136 0.2289 ***Johannesburg Stock Exchange South Africa 0.0010 ** 0.0895 ** 0.1221 *** 0.0455 0.1707 ***Barcelona Stock Exchange Spain 0.0011 ** 0.0768 ** 0.0364 −0.0240 0.1937 ***Stockholm Stock Exchange Sweden 0.0013 *** 0.1268 *** 0.0436 * 0.0219 0.1497 ***CHE SWX Swiss Exchange Switzerland 0.0011 ** 0.0757 ** 0.0610 * 0.0138 0.1935 ***Taiwan Stock Exchange Taiwan 0.0010 ** 0.0076 0.0053 −0.0719 *** 0.2409 ***Stock Exchange of Thailand (SET) Thailand 0.0010 ** 0.0167 0.0229 −0.0218 0.2319 ***SETS (Electronic Trading Service) United Kingdom 0.0012 *** 0.2009 *** 0.1174 ** −0.0420 0.1428 ***LSE-AIM United Kingdom 0.0010 ** 0.0563 ** 0.0049 −0.0121 0.1735 ***International Retail Service (IRS) United Kingdom 0.0012 *** 0.0244 0.1100 *** −0.0179 0.1940 ***New York Stock Exchange United States 0.0015 *** 0.2360 *** 0.0706 ** 0.0607 * 0.1655 ***NASD OTC Bulletin Board United States 0.0015 *** 0.0009 0.0252 * 0.0101 0.2035 ***American Stock Exchange United States 0.0015 *** 0.1067 *** 0.1032 *** 0.0200 0.1856 ***Nasdaq United States 0.0014 *** 0.1194 *** −0.0049 0.0530 *** 0.1688 ***

In the estimation value weighted market return (MRt) and value weighted daily symmetrical percentage change in market percentage spread (MLt) are used.MRother refers to the value weighted market return of the foreign market against which BSE's causal relationship is tested. Wald test is used to test the hypothesisthat the foreign equity market's returns with all of its lags Granger cause BSE market returns (β1=β3=β3=0). Chi2 is the statistic for the Wald test. Data for thestudy is provided by Dicle (2008) based on Reuters through “QuoteCenter” application of Equis International for the time period starting with January, 2000 untilthe end of 2007. Statistical significance levels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.



MRt−2 MRt−3 MLt Arch Garch Arch Cons. Chi2

−0.0937 *** 0.0811 *** −0.0156 *** 0.3501 *** 0.3997 *** 0.0001 *** 4.88−0.1276 *** 0.0473 * −0.0152 *** 0.4741 *** 0.1819 ** 0.0001 *** 17.36 ***−0.0868 *** 0.0637 * −0.0145 *** 0.2729 *** 0.5410 *** 0.0000 *** 1.58−0.0942 *** 0.0578 ** −0.0155 *** 0.3637 *** 0.2506 *** 0.0001 *** 8.07 **−0.0938 *** 0.0578 ** −0.0159 *** 0.3863 *** 0.2395 *** 0.0001 *** 43.06 ***−0.1141 *** 0.0604 ** −0.0212 *** 0.3852 *** 0.3148 0.0000 * 10.77 **−0.0789 *** 0.0490 * −0.0161 *** 0.3807 *** 0.2749 *** 0.0001 *** 4.55−0.0819 *** 0.0455 * −0.0155 *** 0.3959 *** 0.2517 *** 0.0001 *** 4.92−0.1311 *** 0.0321 −0.0163 *** 0.3952 *** 0.2649 *** 0.0001 *** 8.58 **−0.1935 *** 0.0543 −0.0256 *** 0.5523 *** 0.6557 ** 0.0000 3.25−0.0968 *** 0.0642 ** −0.0163 *** 0.3723 *** 0.3062 *** 0.0001 *** 11.99 ***−0.0891 *** 0.0520 −0.0105 *** 0.4211 *** 0.2727 *** 0.0001 *** 3.94−0.1091 *** 0.0675 *** −0.0153 *** 0.4202 *** 0.2520 *** 0.0001 *** 8.66 **−0.1318 *** 0.0620 ** −0.0208 *** 0.3259 *** 0.4149 ** 0.0000 * 2.20−0.1088 *** 0.0673 *** −0.0152 *** 0.4238 *** 0.2485 *** 0.0001 *** 9.56 **−0.0951 *** 0.0651 *** −0.0156 *** 0.4061 *** 0.2676 *** 0.0001 *** 8.20 **−0.0890 *** 0.0522 ** −0.0157 *** 0.3813 *** 0.2762 *** 0.0001 *** 2.62−0.1055 *** 0.0582 ** −0.0151 *** 0.3997 *** 0.2565 *** 0.0001 *** 11.47 ***−0.0977 *** 0.0647 *** −0.0153 *** 0.4416 *** 0.2424 *** 0.0001 *** 15.48 ***−0.0827 *** 0.0695 *** −0.0155 *** 0.3876 *** 0.1318 0.0001 *** 5.28−0.1000 *** 0.0687 ** −0.0162 *** 0.3030 *** 0.4031 *** 0.0001 *** 14.55 ***−0.0823 *** 0.0668 *** −0.0159 *** 0.3699 *** 0.2274 *** 0.0001 *** 5.92−0.1045 *** 0.0527 ** −0.0153 *** 0.3970 *** 0.2432 *** 0.0001 *** 19.51 ***−0.0905 *** 0.0750 *** −0.0150 *** 0.3994 *** 0.2371 *** 0.0001 *** 11.35 ***−0.1286 *** 0.0627 ** −0.0165 *** 0.3374 *** 0.2520 ** 0.0001 *** 2.38−0.0744 ** 0.0483 * −0.0147 *** 0.3134 *** 0.4207 *** 0.0001 *** 18.31 ***−0.1063 *** 0.0486 −0.0190 *** 0.2323 *** 0.1648 0.0001 ** 2.81−0.1340 *** 0.0216 −0.0180 *** 0.1886 *** 0.5746 *** 0.0000 3.07−0.0611 * 0.0600 ** −0.0168 *** 0.2589 *** 0.4208 *** 0.0001 *** 0.54−0.0733 ** 0.0484 * −0.0169 *** 0.2947 *** 0.2360 ** 0.0001 *** 0.90−0.1027 *** 0.0724 *** −0.0171 *** 0.3980 *** 0.2234 ** 0.0001 *** 2.40−0.1099 *** 0.0735 ** −0.0147 *** 0.3138 *** 0.4842 *** 0.0001 *** 1.31−0.1205 *** 0.0466 * −0.0155 *** 0.3362 *** 0.4388 *** 0.0001 *** 8.93 **−0.1184 *** 0.0341 −0.0150 *** 0.3850 *** 0.2656 *** 0.0001 *** 3.09−0.1019 *** 0.0457 * −0.0159 *** 0.3630 *** 0.3831 *** 0.0001 *** 5.07−0.1108 *** 0.0658 ** −0.0170 *** 0.3626 *** 0.2933 *** 0.0001 *** 1.86−0.1266 *** 0.0537 ** −0.0155 *** 0.4276 *** 0.2406 *** 0.0001 *** 19.80 ***−0.1257 *** 0.0545 ** −0.0157 *** 0.4182 *** 0.2136 *** 0.0001 *** 7.93 **−0.1139 *** 0.0433 * −0.0154 *** 0.3846 *** 0.2589 ** 0.0001 *** 28.41 ***−0.1152 *** 0.0479 * −0.0154 *** 0.4216 *** 0.1810 ** 0.0001 *** 8.88 **−0.1127 *** 0.0866 *** −0.0169 *** 0.4491 *** 0.0291 0.0001 *** 8.45 **−0.1057 *** 0.0962 *** −0.0163 *** 0.4477 *** 0.1629 0.0001 *** 2.10−0.1223 *** 0.0564 ** −0.0153 *** 0.4479 *** 0.1773 ** 0.0001 *** 28.45 ***−0.0926 *** 0.0553 ** −0.0154 *** 0.3729 *** 0.2711 *** 0.0001 *** 5.48−0.1462 *** 0.0618 ** −0.0157 *** 0.4112 *** 0.1827 ** 0.0001 *** 17.86 ***−0.1231 *** 0.0457 * −0.0155 *** 0.4788 *** 0.1396 * 0.0001 *** 41.48 ***−0.1121 *** 0.0568 ** −0.0156 *** 0.4194 *** 0.1373 0.0001 *** 3.42−0.1239 *** 0.0408 * −0.0148 *** 0.4859 *** 0.0764 0.0001 *** 31.10 ***−0.1309 *** 0.0399 −0.0162 *** 0.4046 *** 0.2171 ** 0.0001 *** 42.22 ***


Table 9Results for market-wide Granger type causal relationship between for Indian National Exchange (INE) returns and international equity markets' returns usingEq. (3).


Exchange Country Constant MRother,t−1 MRother,t−2 MRother,t−3 MRt−1

National Automated Trading Australia 0.0009 ** −0.0864 * 0.0779 −0.0254 0.2942 ***Vienna Stock Exchange Austria 0.0009 ** 0.0261 0.0730 −0.0635 0.2449 ***Euronext Brussels Belgium 0.0005 −0.0051 0.0341 0.0154 0.2306 ***TSX Venture Exchange Canada 0.0006 0.0796 ** 0.0431 0.0071 0.2578 ***Toronto Stock Exchange Canada 0.0008 * 0.2413 *** 0.0449 0.0808 ** 0.2218 ***Santiago Stock Exchange Chile 0.0008 0.1653 ** 0.0089 0.0214 0.3410 ***Shanghai Stock Exchange China 0.0007 0.0116 0.0320 −0.0046 0.2503 ***Shenzhen Stock Exchange China 0.0008 * −0.0004 0.0393 −0.0206 0.2547 ***Copenhagen Stock Exchange Denmark 0.0008 ** 0.0633 ** 0.0541 0.0551 * 0.2461 ***Cairo Stock Exchange Egypt 0.0023 *** 0.0794 −0.0300 −0.0363 0.2895 ***Helsinki Stock Exchange Finland 0.0007 * 0.0519 *** −0.0039 0.0042 0.2537 ***Euronext Paris France 0.0004 0.0395 0.0193 0.0027 0.2129 ***Berlin Stock Exchange Germany 0.0009 ** 0.1041 ** 0.0331 −0.0130 0.2624 ***Stuttgart Stock Exchange Germany 0.0010 * −0.0023 0.0282 −0.0093 0.3175 ***Munich Stock Exchange Germany 0.0009 ** 0.0893 * 0.0479 −0.0181 0.2644 ***Frankfurt Stock Exchange Germany 0.0009 ** 0.0351 −0.0449 0.0020 0.2710 ***XETRA Germany 0.0010 ** 0.0390 ** 0.0101 0.0071 0.2739 ***Duesseldorf Stock Exchange Germany 0.0009 ** 0.0714 *** 0.0290 0.0107 0.2617 ***Hamburg Stock Exchange Germany 0.0008 ** 0.0929 ** −0.0249 −0.0280 0.2631 ***Athens Stock Exchange Greece 0.0008 * 0.0077 0.0641 ** 0.0180 0.3037 ***Hong Kong Stock Exchange Hong Kong 0.0005 −0.0603 * 0.0909 *** 0.0077 0.2918 ***Jakarta Stock Exchange Indonesia 0.0009 ** −0.0565 ** 0.0157 −0.0252 0.2928 ***Milano Stock Exchange Italy 0.0009 ** 0.1038 *** −0.0037 0.0812 ** 0.2636 ***Tokyo Stock Exchange Japan 0.0010 ** 0.0473 * 0.0615 ** 0.0336 0.2512 ***Osaka Stock Exchange Japan 0.0010 ** 0.0040 0.0690 * 0.0378 0.2819 ***Japan SDAQS Japan 0.0010 ** −0.0624 ** 0.0771 ** 0.0480 * 0.2631 ***NASDAQ Japan Japan 0.0011 ** −0.0154 0.0432 *** 0.0186 0.2680 ***Amman Financial Market Jordan 0.0010 ** 0.0492 0.0202 −0.0805 0.2519 ***Korea Stock Exchange Korea 0.0009 ** −0.0177 0.0087 0.0027 0.2811 ***KOSDAQ Korea 0.0012 *** −0.0006 0.0011 0.0243 0.2583 ***Kuala Lumpur Stock Exchange Malaysia 0.0008 * −0.0295 −0.0199 −0.0275 0.2873 ***Euronext Amsterdam Netherlands 0.0005 0.0320 −0.0116 0.0142 0.2095 ***New Zealand Stock Exchange New Zealand 0.0009 ** −0.0910 ** 0.1122 ** 0.0285 0.2551 ***Oslo Stock Exchange Norway 0.0009 ** 0.0142 −0.0095 −0.0152 0.2613 ***Lima Stock Exchange Peru 0.0008 * 0.0546 0.0747 −0.0524 0.2776 ***Stock Exchange of Singapore Singapore 0.0009 ** −0.0345 0.0570 0.0107 0.3076 ***Johannesburg Stock Exchange South Africa 0.0008 * 0.1099 *** 0.1050 *** 0.0652 * 0.2520 ***Barcelona Stock Exchange Spain 0.0010 ** 0.0791 ** 0.0246 0.0079 0.2651 ***Stockholm Stock Exchange Sweden 0.0011 *** 0.1315 *** 0.0316 0.0469 * 0.2328 ***CHE SWX Swiss Exchange Switzerland 0.0009 ** 0.0681 ** 0.0561 0.0328 0.2846 ***Taiwan Stock Exchange Taiwan 0.0007 * −0.0212 −0.0091 −0.0414 0.2979 ***Stock Exchange of Thailand (SET) Thailand 0.0009 ** 0.0264 0.0232 −0.0041 0.2745 ***SETS (Electronic Trading Service) United Kingdom 0.0009 ** 0.1860 *** 0.0778 0.0239 0.2167 ***LSE-AIM United Kingdom 0.0008 * 0.0266 0.0094 −0.0044 0.2513 ***International Retail Service (IRS) United Kingdom 0.0009 ** 0.0416 ** 0.0840 *** 0.0059 0.2459 ***New York Stock Exchange United States 0.0012 *** 0.2097 *** 0.0225 0.1045 *** 0.2512 ***NASD OTC Bulletin Board United States 0.0012 *** 0.0016 0.0209 0.0074 0.2760 ***American Stock Exchange United States 0.0011 *** 0.1099 *** 0.0947 *** 0.0057 0.2612 ***Nasdaq United States 0.0011 *** 0.1126 *** −0.0219 0.0529 *** 0.2320 ***

In the estimation value weighted market return (MRt) and value weighted daily symmetrical percentage change in market percentage spread (MLt) are used.MRother refers to the value weighted market return of the foreign market against which INE's causal relationship is tested. Wald test is used to test the hypothesisthat the foreign equity market's returns with all of its lags Granger cause INE market returns β1=β3=β3=0. Chi2 is the statistic for the Wald test. Data for thestudy is provided by Dicle (2008) based on Reuters through “QuoteCenter” application of Equis International for the time period starting with January, 2000 untilthe end of 2007. Statistical significance levels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.



MRt−2 MRt−3 MLt Arch Garch Arch Cons. Chi2

−0.0770 ** 0.0400 −0.0091 *** 0.3327 *** 0.4129 *** 0.0001 *** 5.39−0.0976 *** 0.0276 −0.0089 *** 0.3692 *** 0.3614 *** 0.0001 *** 4.66−0.1026 *** 0.0392 −0.0093 *** 0.3758 *** 0.3440 *** 0.0001 *** 0.99−0.0780 ** 0.0350 −0.0091 *** 0.3373 *** 0.2889 *** 0.0001 *** 7.32 *−0.0785 *** 0.0374 −0.0085 *** 0.3762 *** 0.2765 *** 0.0001 *** 59.44 ***−0.0016 0.0299 −0.0091 *** 0.4586 *** 0.2286 * 0.0000 *** 5.96−0.0785 ** 0.0105 −0.0098 *** 0.3055 *** 0.4199 *** 0.0001 *** 1.33−0.0812 *** 0.0043 −0.0097 *** 0.3109 *** 0.4011 *** 0.0001 *** 2.08−0.1155 *** 0.0105 −0.0084 *** 0.3433 *** 0.3328 *** 0.0001 *** 8.94 **−0.0710 0.0659 * −0.0127 *** 0.4157 *** 1.1395 *** −0.0001 ** 3.73−0.0796 *** 0.0346 −0.0091 *** 0.3186 *** 0.4526 *** 0.0000 *** 10.43 **−0.1332 *** 0.0560 −0.0086 *** 0.3711 *** 0.3666 *** 0.0001 *** 1.47−0.0875 *** 0.0361 −0.0089 *** 0.3458 *** 0.3274 *** 0.0001 *** 4.92−0.0326 0.0158 −0.0095 *** 0.2992 *** 0.4466 *** 0.0000 * 0.60−0.0903 *** 0.0367 −0.0089 *** 0.3513 *** 0.3327 *** 0.0001 *** 6.09−0.0732 ** 0.0363 −0.0087 *** 0.3431 *** 0.3396 *** 0.0001 *** 2.32−0.0760 *** 0.0386 −0.0087 *** 0.3588 *** 0.3248 *** 0.0001 *** 5.31−0.0889 *** 0.0318 −0.0088 *** 0.3516 *** 0.3261 *** 0.0001 *** 9.59 **−0.0738 ** 0.0365 −0.0087 *** 0.3431 *** 0.3626 *** 0.0001 *** 6.51*−0.0698 ** 0.0400 −0.0087 *** 0.3592 *** 0.2142 ** 0.0001 *** 5.64−0.0866 *** 0.0421 −0.0105 *** 0.2989 *** 0.3613 *** 0.0001 *** 11.28 **−0.0904 *** 0.0614 ** −0.0103 *** 0.3596 *** 0.2658 *** 0.0001 *** 6.38 *−0.0846 *** 0.0217 −0.0085 *** 0.3336 *** 0.3227 *** 0.0001 *** 15.74 ***−0.0748 *** 0.0282 −0.0088 *** 0.3545 *** 0.3124 *** 0.0001 *** 12.53 ***−0.1050 *** 0.0351 −0.0089 *** 0.3124 *** 0.3719 *** 0.0001 *** 4.39−0.0872 *** 0.0103 −0.0090 *** 0.3426 *** 0.4021 *** 0.0001 *** 21.95 ***−0.1029 *** 0.0213 −0.0084 *** 0.2956 *** 0.3645 ** 0.0001 *** 9.02 **−0.0557 0.0185 −0.0094 *** 0.3549 *** 0.3121 * 0.0001 ** 2.99−0.0658 * 0.0364 −0.0092 *** 0.2927 *** 0.3756 *** 0.0001 *** 1.00−0.0637 * 0.0312 −0.0086 *** 0.2946 *** 0.3377 *** 0.0001 *** 1.69−0.0834 *** 0.0426 −0.0096 *** 0.3315 *** 0.3249 *** 0.0001 *** 2.67−0.1173 *** 0.0499 −0.0099 *** 0.3619 *** 0.3703 *** 0.0001 *** 1.57−0.0957 *** 0.0198 −0.0095 *** 0.3220 *** 0.4838 *** 0.0000 *** 13.15 ***−0.0982 *** 0.0146 −0.0091 *** 0.3293 *** 0.3816 *** 0.0001 *** 3.86−0.0913 *** 0.0201 −0.0086 *** 0.4100 *** 0.2844 *** 0.0001 *** 4.57−0.1008 *** 0.0289 −0.0101 *** 0.3469 *** 0.3322 *** 0.0001 *** 3.26−0.1071 *** 0.0215 −0.0089 *** 0.3930 *** 0.2406 *** 0.0001 *** 24.22 ***−0.0954 *** 0.0224 −0.0086 *** 0.3427 *** 0.3083 *** 0.0001 *** 7.47 *−0.1042 *** 0.0139 −0.0082 *** 0.3433 *** 0.3334 *** 0.0001 *** 36.19 ***−0.0961 *** 0.0175 −0.0093 *** 0.3666 *** 0.2209 ** 0.0001 *** 9.83**−0.0925 *** 0.0474 −0.0098 *** 0.3264 *** 0.1991 0.0001 *** 3.80−0.0679 ** 0.0486 −0.0082 *** 0.3350 *** 0.2659 * 0.0001 *** 1.78−0.0880 *** 0.0184 −0.0093 *** 0.3542 *** 0.2954 *** 0.0001 *** 25.30 ***−0.0758 ** 0.0315 −0.0095 *** 0.3325 *** 0.3258 *** 0.0001 *** 1.30−0.1025 *** 0.0316 −0.0095 *** 0.3289 *** 0.3396 *** 0.0001 *** 14.13***−0.1017 *** 0.0201 −0.0099 *** 0.3608 *** 0.2658 ** 0.0001 *** 36.79 ***−0.0821 *** 0.0307 −0.0100 *** 0.3539 *** 0.2653 ** 0.0001 *** 2.33−0.0894 *** 0.0232 −0.0102 *** 0.3848 *** 0.1313 0.0001 *** 42.14 ***−0.0994 *** 0.0145 −0.0101 *** 0.3261 *** 0.4562 *** 0.0000 *** 39.31 ***


Table 10Results for relationship between Bombay Stock Exchange (BSE) listed stocks' returns and international equity markets' listed stocks' returns with panel dataestimation using Eq. (4).


Exchange Country Constant Rt−1 Rt−2 Rt−3 MRt ΔR2

National Automated Trading Australia −0.0001 ** −0.0590 *** −0.0311 *** −0.0124 *** 0.7316 *** 0.6526 ***Vienna Stock Exchange Austria −0.0001 ** −0.0624 *** −0.0319 *** −0.0131 *** 0.7224 *** 0.6505 ***Euronext Brussels Belgium −0.0001 −0.1046 *** −0.0490 *** −0.0223 *** 0.6574 *** 0.4675 ***TSX Venture Exchange Canada −0.0001 *** −0.0589 *** −0.0288 *** −0.0114 *** 0.7327 *** 0.6475 ***Toronto Stock Exchange Canada 0.0000 −0.0583 *** −0.0287 *** −0.0115 *** 0.7369 *** 0.6500 ***Santiago Stock Exchange Chile 0.0001 ** −0.0204 *** −0.0175 *** −0.0079 *** 0.8312 *** 0.9422 ***Shanghai Stock Exchange China 0.0001 *** −0.0599 *** −0.0317 *** −0.0138 *** 0.7292 *** 0.6698 ***Shenzhen Stock Exchange China 0.0001 *** −0.0598 *** −0.0317 *** −0.0137 *** 0.7297 *** 0.6710 ***Copenhagen Stock Exchange Denmark −0.0001 * −0.0596 *** −0.0319 *** −0.0131 *** 0.7264 *** 0.6573 ***Cairo Stock Exchange Egypt −0.0003 *** −0.0340 *** −0.0193 *** −0.0110 *** 0.7735 *** 0.9192 ***Helsinki Stock Exchange Finland −0.0001 *** −0.0595 *** −0.0305 *** −0.0122 *** 0.7277 *** 0.6424 ***Euronext Paris France −0.0003 *** −0.1114 *** −0.0521 *** −0.0242 *** 0.6098 *** 0.4334 ***Berlin Stock Exchange Germany −0.0001 *** −0.0597 *** −0.0316 *** −0.0118 *** 0.7308 *** 0.6486 ***Stuttgart Stock Exchange Germany −0.0001 ** −0.0193 *** −0.0175 *** −0.0070 *** 0.8315 *** 0.8747 ***Munich Stock Exchange Germany −0.0001 ** −0.0597 *** −0.0314 *** −0.0120 *** 0.7305 *** 0.6487 ***Frankfurt Stock Exchange Germany −0.0001 ** −0.0598 *** −0.0317 *** −0.0121 *** 0.7310 *** 0.6485 ***XETRA Germany −0.0001 ** −0.0589 *** −0.0307 *** −0.0114 *** 0.7348 *** 0.6524 ***Duesseldorf Stock Exchange Germany −0.0001 ** −0.0597 *** −0.0314 *** −0.0123 *** 0.7294 *** 0.6486 ***Hamburg Stock Exchange Germany −0.0001 *** −0.0600 *** −0.0318 *** −0.0120 *** 0.7304 *** 0.6485 ***Athens Stock Exchange Greece −0.0001 *** −0.0586 *** −0.0310 *** −0.0131 *** 0.7359 *** 0.6446 ***Hong Kong Stock Exchange Hong Kong 0.0000 −0.0591 *** −0.0315 *** −0.0119 *** 0.7356 *** 0.6582 ***Jakarta Stock Exchange Indonesia −0.0001 *** −0.0611 *** −0.0314 *** −0.0124 *** 0.7447 *** 0.6284 ***Milano Stock Exchange Italy −0.0001 ** −0.0600 *** −0.0315 *** −0.0124 *** 0.7270 *** 0.6482 ***Tokyo Stock Exchange Japan 0.0001 ** −0.0572 *** −0.0300 *** −0.0113 *** 0.7436 *** 0.6719 ***Osaka Stock Exchange Japan 0.0002 *** −0.0474 *** −0.0265 *** −0.0093 *** 0.7899 *** 0.7240 ***Japan SDAQS Japan 0.0001 ** −0.0586 *** −0.0304 *** −0.0119 *** 0.7383 *** 0.6614 ***NASDAQ Japan Japan 0.0002 *** −0.0432 *** −0.0242 *** −0.0096 *** 0.8000 *** 0.7571 ***Amman Financial Market Jordan −0.0001 * −0.0607 *** −0.0286 *** −0.0192 *** 0.8227 *** 0.8115 ***Korea Stock Exchange Korea 0.0002 *** −0.0620 *** −0.0333 *** −0.0139 *** 0.7243 *** 0.6877 ***KOSDAQ Korea 0.0003 *** −0.0571 *** −0.0313 *** −0.0127 *** 0.7364 *** 0.7171 ***Kuala Lumpur Stock Exchange Malaysia 0.0000 −0.0583 *** −0.0319 *** −0.0127 *** 0.7374 *** 0.6594 ***Euronext Amsterdam Netherlands 0.0000 −0.0949 *** −0.0443 *** −0.0192 *** 0.6873 *** 0.4972 ***New Zealand Stock Exchange New Zealand −0.0001 ** −0.0614 *** −0.0323 *** −0.0128 *** 0.7286 *** 0.6488 ***Oslo Stock Exchange Norway 0.0000 −0.0599 *** −0.0319 *** −0.0123 *** 0.7200 *** 0.6618 ***Lima Stock Exchange Peru −0.0002 *** −0.0582 *** −0.0308 *** −0.0127 *** 0.7415 *** 0.6457 ***Stock Exchange of Singapore Singapore −0.0001 *** −0.0600 *** −0.0326 *** −0.0134 *** 0.7317 *** 0.6387 ***Johannesburg Stock Exchange South Africa −0.0001 *** −0.0597 *** −0.0323 *** −0.0120 *** 0.7290 *** 0.6491 ***Barcelona Stock Exchange Spain −0.0001 *** −0.0595 *** −0.0312 *** −0.0124 *** 0.7274 *** 0.6451 ***Stockholm Stock Exchange Sweden −0.0001 * −0.0596 *** −0.0310 *** −0.0127 *** 0.7225 *** 0.6503 ***CHE SWX Swiss Exchange Switzerland 0.0000 −0.0579 *** −0.0312 *** −0.0125 *** 0.7335 *** 0.6525 ***Taiwan Stock Exchange Taiwan 0.0002 *** −0.0591 *** −0.0325 *** −0.0152 *** 0.7296 *** 0.6721 ***Stock Exchange of Thailand (SET) Thailand −0.0002 *** −0.0604 *** −0.0311 *** −0.0125 *** 0.7346 *** 0.6512 ***SETS (Electronic Trading Service) United Kingdom −0.0001 *** −0.0597 *** −0.0316 *** −0.0124 *** 0.7276 *** 0.6448 ***LSE-AIM United Kingdom −0.0001 *** −0.0597 *** −0.0313 *** −0.0115 *** 0.7301 *** 0.6443 ***International Retail Service (IRS) United Kingdom 0.0000 −0.0525 *** −0.0294 *** −0.0103 *** 0.7568 *** 0.6956 ***New York Stock Exchange United States 0.0000 −0.0585 *** −0.0309 *** −0.0129 *** 0.7250 *** 0.6577 ***NASD OTC Bulletin Board United States 0.0000 −0.0591 *** −0.0306 *** −0.0127 *** 0.7250 *** 0.6572 ***American Stock Exchange United States 0.0000 −0.0587 *** −0.0308 *** −0.0130 *** 0.7250 *** 0.6576 ***Nasdaq United States 0.0000 −0.0588 *** −0.0307 *** −0.0130 *** 0.7275 *** 0.6573 ***

In the estimation, daily returns (Rt) for individual stocks, value weighted market return (MRt), change in squared daily return (ΔR2) and daily change in liquidity(Lt) for individual stocks are used. MRother refers to the value weighted market return of the foreign market against which BSE's individual stocks' relationship istested. Market return is calculated separately for each stock by excluding the stock. In other words, for stock i, there exists MRi,t, which is the value weightedmarket return excluding the stock i. Wald test is used to test the hypothesis that the foreign equity market's returns with all of its lags Granger cause BSE listedstocks' returns (ϑ1=ϑ3=ϑ3=0). Wald test is employed on a similar model that is estimated individually rather than on the panel data estimation. Wald (1%)refers to the percentage of BSE that has Wald test statistic that is statistically significant at 1% or better. Data for the study is provided by Dicle (2008) based onReuters through “QuoteCenter” application of Equis International for the time period starting with January, 2000 until the end of 2007. Statistical significancelevels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.



Lt−1 MRother,t−1 MRother,t−2 MRother,t−3 R2 Wald (1%) Wald (5%) Wald (10%)

−0.0032 *** −0.0082 * 0.0362 *** 0.0170 *** 0.0924 1.39% 5.99% 12.22%−0.0032 *** 0.0536 *** 0.0396 *** 0.0000 0.0918 1.31% 5.50% 11.98%−0.0043 *** 0.0421 *** 0.0164 *** 0.0177 *** 0.0727 1.94% 9.40% 17.15%−0.0032 *** 0.0282 *** −0.0024 0.0052 * 0.0936 1.39% 6.23% 12.63%−0.0033 *** 0.0012 −0.0212 *** 0.0171 *** 0.0937 2.21% 8.61% 16.16%−0.0028 *** −0.0209 *** 0.0002 0.0081 0.1211 1.50% 6.76% 13.01%−0.0032 *** −0.0092 *** 0.0078 *** 0.0102 *** 0.0926 1.07% 5.25% 11.48%−0.0032 *** −0.0172 *** 0.0045 ** 0.0053 *** 0.0927 1.64% 7.22% 13.29%−0.0032 *** −0.0041 0.0184 *** 0.0335 *** 0.0920 1.64% 7.38% 13.13%−0.0029 *** −0.0031 −0.0236 *** −0.0058 0.1234 1.40% 4.62% 10.73%−0.0032 *** 0.0057 *** 0.0071 *** 0.0098 *** 0.0922 1.56% 6.48% 11.73%−0.0043 *** 0.0247 *** 0.0116 *** 0.0233 *** 0.0646 1.00% 4.59% 8.48%−0.0032 *** 0.0075 * 0.0411 *** −0.0019 0.0925 0.98% 5.00% 10.42%−0.0029 *** 0.0132 *** 0.0226 *** 0.0269 *** 0.1198 1.00% 5.92% 13.34%−0.0032 *** 0.0099 *** 0.0261 *** 0.0148 *** 0.0925 0.82% 5.58% 11.81%−0.0032 *** 0.0120 *** 0.0333 *** 0.0072 ** 0.0926 1.31% 6.23% 11.65%−0.0032 *** 0.0015 0.0046 ** −0.0051 ** 0.0930 0.66% 5.17% 10.58%−0.0032 *** 0.0053 ** 0.0063 ** 0.0290 *** 0.0925 2.79% 7.88% 14.60%−0.0032 *** 0.0172 *** 0.0481 *** 0.0047 0.0926 1.97% 6.32% 11.73%−0.0032 *** 0.0230 *** 0.0249 *** 0.0337 *** 0.0934 1.48% 7.63% 14.77%−0.0032 *** −0.0213 *** 0.0133 *** 0.0272 *** 0.0935 2.71% 9.76% 16.08%−0.0032 *** 0.0149 *** 0.0162 *** 0.0080 *** 0.0931 1.39% 5.91% 11.90%−0.0032 *** 0.0242 *** 0.0116 *** 0.0313 *** 0.0925 2.38% 7.14% 12.63%−0.0032 *** −0.0098 *** 0.0320 *** 0.0059 ** 0.0952 1.64% 7.38% 13.04%−0.0030 *** 0.0023 0.0374 *** 0.0091 *** 0.1050 1.81% 6.35% 13.11%−0.0032 *** 0.0020 0.0229 *** 0.0190 *** 0.0944 0.98% 5.41% 11.24%−0.0030 *** −0.0041 ** 0.0019 0.0091 *** 0.1058 1.56% 6.57% 11.91%−0.0030 *** 0.0787 *** 0.0465 *** −0.0111 ** 0.1051 1.42% 5.66% 11.41%−0.0032 *** 0.0146 *** 0.0171 *** 0.0083 *** 0.0923 1.72% 7.63% 14.27%−0.0031 *** 0.0159 *** 0.0033 * −0.0049 *** 0.0956 1.48% 7.30% 13.95%−0.0032 *** 0.0006 0.0324 *** 0.0046 0.0940 2.46% 6.89% 11.73%−0.0042 *** 0.0224 *** 0.0085 *** 0.0129 *** 0.0786 0.87% 4.82% 9.26%−0.0032 *** 0.0184 *** 0.0431 *** 0.0176 *** 0.0922 1.07% 4.84% 10.99%−0.0032 *** −0.0010 −0.0006 0.0031 ** 0.0896 1.07% 5.74% 11.57%−0.0032 *** 0.0015 0.0023 0.0060 * 0.0944 0.82% 4.10% 8.45%−0.0033 *** 0.0116 *** 0.0365 *** 0.0364 *** 0.0926 2.13% 8.29% 14.27%−0.0033 *** 0.0244 *** 0.0217 *** 0.0057 * 0.0937 1.39% 5.74% 10.99%−0.0032 *** 0.0188 *** 0.0262 *** 0.0202 *** 0.0927 1.48% 7.47% 13.62%−0.0032 *** 0.0110 *** 0.0011 0.0304 *** 0.0919 2.46% 8.29% 14.11%−0.0032 *** 0.0230 *** 0.0251 *** 0.0271 *** 0.0936 1.39% 6.89% 12.39%−0.0032 *** 0.0182 *** 0.0172 *** 0.0211 *** 0.0926 1.15% 5.74% 12.14%−0.0032 *** 0.0143 *** 0.0130 *** 0.0222 *** 0.0931 1.56% 7.14% 13.04%−0.0032 *** 0.0189 *** 0.0174 *** 0.0415 *** 0.0922 2.30% 7.22% 14.44%−0.0032 *** 0.0044 * 0.0188 *** −0.0098 *** 0.0921 1.48% 6.15% 11.07%−0.0031 *** 0.0265 *** 0.0224 *** 0.0217 *** 0.0991 1.64% 6.48% 12.14%−0.0032 *** 0.0013 −0.0094 *** 0.0409 *** 0.0922 2.46% 9.93% 17.39%−0.0032 *** −0.0029 *** −0.0001 0.0043 *** 0.0917 0.82% 6.07% 10.91%−0.0032 *** 0.0089 *** −0.0058 ** 0.0360 *** 0.0922 2.54% 7.79% 15.01%−0.0032 *** −0.0146 *** −0.0121 *** 0.0173 *** 0.0918 3.86% 11.98% 20.92%


Table 11Results for relationship between Indian National Exchange (INE) listed stocks' returns and international equity markets' listed stocks' returns with panel dataestimation using Eq. (5).


Exchange Country Constant Rt−1 Rt−2 Rt−3 MRt ΔR2

National Automated Trading Australia −0.0004 *** 0.0071 *** −0.0175 *** 0.0009 0.7686 *** 0.7441 ***Vienna Stock Exchange Austria −0.0004 *** −0.0003 −0.0192 *** −0.0001 0.7618 *** 0.7277 ***Euronext Brussels Belgium −0.0004 *** 0.0006 −0.0233 *** −0.0004 0.8017 *** 0.6085 ***TSX Venture Exchange Canada −0.0004 *** 0.0056 *** −0.0167 *** 0.0022 0.7670 *** 0.7521 ***Toronto Stock Exchange Canada −0.0004 *** 0.0064 *** −0.0161 *** 0.0026 0.7700 *** 0.7559 ***Santiago Stock Exchange Chile −0.0001 ** 0.0086 *** −0.0157 *** −0.0005 0.7789 *** 1.0042 ***Shanghai Stock Exchange China −0.0003 *** 0.0027 −0.0184 *** 0.0002 0.7661 *** 0.7364 ***Shenzhen Stock Exchange China −0.0003 *** 0.0026 −0.0185 *** 0.0002 0.7668 *** 0.7396 ***Copenhagen Stock Exchange Denmark −0.0004 *** 0.0036 ** −0.0187 *** 0.0001 0.7641 *** 0.7380 ***Cairo Stock Exchange Egypt −0.0002 ** −0.0031 −0.0241 *** 0.0048 0.7319 *** 1.1484 ***Helsinki Stock Exchange Finland −0.0004 *** 0.0035 ** −0.0181 *** 0.0011 0.7631 *** 0.7228 ***Euronext Paris France −0.0005 *** 0.0031 −0.0226 *** −0.0018 0.7834 *** 0.6481 ***Berlin Stock Exchange Germany −0.0004 *** 0.0050 *** −0.0187 *** 0.0010 0.7681 *** 0.7402 ***Stuttgart Stock Exchange Germany −0.0003 *** 0.0081 *** −0.0180 *** 0.0014 0.7877 *** 0.9405 ***Munich Stock Exchange Germany −0.0004 *** 0.0042 *** −0.0189 *** 0.0009 0.7663 *** 0.7397 ***Frankfurt Stock Exchange Germany −0.0004 *** 0.0040 ** −0.0189 *** 0.0010 0.7653 *** 0.7397 ***XETRA Germany −0.0004 *** 0.0049 *** −0.0182 *** 0.0017 0.7689 *** 0.7426 ***Duesseldorf Stock Exchange Germany −0.0004 *** 0.0040 ** −0.0189 *** 0.0007 0.7652 *** 0.7396 ***Hamburg Stock Exchange Germany −0.0004 *** 0.0040 ** −0.0190 *** 0.0015 0.7668 *** 0.7393 ***Athens Stock Exchange Greece −0.0004 *** 0.0055 *** −0.0183 *** −0.0003 0.7711 *** 0.7389 ***Hong Kong Stock Exchange Hong Kong −0.0003 *** 0.0082 *** −0.0172 *** 0.0001 0.7686 *** 0.7650 ***Jakarta Stock Exchange Indonesia −0.0004 *** 0.0050 *** −0.0176 *** −0.0011 0.7700 *** 0.7564 ***Milano Stock Exchange Italy −0.0004 *** 0.0040 ** −0.0189 *** 0.0012 0.7635 *** 0.7395 ***Tokyo Stock Exchange Japan −0.0003 *** 0.0082 *** −0.0190 *** 0.0011 0.7754 *** 0.7778 ***Osaka Stock Exchange Japan −0.0002 *** 0.0101 *** −0.0191 *** 0.0012 0.8102 *** 0.8415 ***Japan SDAQS Japan −0.0003 *** 0.0074 *** −0.0174 *** −0.0007 0.7692 *** 0.7370 ***NASDAQ Japan Japan −0.0001 * 0.0112 *** −0.0184 *** −0.0020 0.8005 *** 0.8891 ***Amman Financial Market Jordan 0.0002 *** 0.0043 * −0.0332 *** 0.0012 0.8133 *** 1.1708 ***Korea Stock Exchange Korea −0.0002 *** 0.0026 −0.0172 *** 0.0006 0.7642 *** 0.8032 ***KOSDAQ Korea −0.0001 * 0.0073 *** −0.0187 *** 0.0001 0.7700 *** 0.8504 ***Kuala Lumpur Stock Exchange Malaysia −0.0003 *** 0.0023 −0.0196 *** −0.0024 0.7623 *** 0.7640 ***Euronext Amsterdam Netherlands −0.0004 *** 0.0018 −0.0213 *** −0.0015 0.8123 *** 0.6149 ***New Zealand Stock Exchange New Zealand −0.0004 *** 0.0026 −0.0193 *** 0.0001 0.7619 *** 0.7244 ***Oslo Stock Exchange Norway −0.0004 *** 0.0048 *** −0.0191 *** 0.0023 0.7592 *** 0.7689 ***Lima Stock Exchange Peru −0.0005 *** 0.0039 ** −0.0199 *** 0.0010 0.7745 *** 0.7180 ***Stock Exchange of Singapore Singapore −0.0004 *** 0.0066 *** −0.0177 *** −0.0017 0.7689 *** 0.7574 ***Johannesburg Stock Exchange South Africa −0.0004 *** 0.0042 ** −0.0193 *** 0.0017 0.7707 *** 0.7153 ***Barcelona Stock Exchange Spain −0.0005 *** 0.0032 ** −0.0195 *** 0.0002 0.7654 *** 0.7135 ***Stockholm Stock Exchange Sweden −0.0004 *** 0.0045 *** −0.0179 *** 0.0010 0.7618 *** 0.7373 ***CHE SWX Swiss Exchange Switzerland −0.0004 *** 0.0039 ** −0.0193 *** −0.0010 0.7675 *** 0.7304 ***Taiwan Stock Exchange Taiwan −0.0002 *** 0.0065 *** −0.0180 *** −0.0019 0.7648 *** 0.8038 ***Stock Exchange of Thailand (SET) Thailand −0.0004 *** 0.0061 *** −0.0188 *** −0.0004 0.7653 *** 0.8216 ***SETS (Electronic Trading Service) United Kingdom −0.0004 *** 0.0044 *** −0.0182 *** 0.0012 0.7711 *** 0.7186 ***LSE-AIM United Kingdom −0.0004 *** 0.0033 ** −0.0183 *** 0.0020 0.7691 *** 0.7164 ***International Retail Service (IRS) United Kingdom −0.0003 *** 0.0029 * −0.0207 *** 0.0021 0.7931 *** 0.7755 ***New York Stock Exchange United States −0.0003 *** 0.0062 *** −0.0169 *** 0.0013 0.7585 *** 0.7705 ***NASD OTC Bulletin Board United States −0.0004 *** 0.0048 *** −0.0169 *** 0.0011 0.7570 *** 0.7727 ***American Stock Exchange United States −0.0003 *** 0.0057 *** −0.0171 *** 0.0011 0.7599 *** 0.7704 ***Nasdaq United States −0.0003 *** 0.0057 *** −0.0167 *** 0.0010 0.7564 *** 0.7733 ***

In the estimation, daily returns (Rt) for individual stocks, value weighted market return (MRt), change in squared daily return (ΔR2) and daily change in liquidity(Lt) for individual stocks are used. MRother refers to the value weighted market return of the foreign market against which INE's individual stocks' relationship istested. Market return is calculated separately for each stock by excluding the stock. In other words, for stock i, there exists MRi,t, which is the value weightedmarket return excluding the stock i. Wald test is used to test the hypothesis that the foreign equity market's returns with all of its lags Granger cause INE listedstocks' returns (ϑ1=ϑ3=ϑ3=0). Wald test is employed on a similar model that is estimated individually rather than on the panel data estimation. Wald (1%)refers to the percentage of INE that has Wald test statistic that is statistically significant at 1% or better. Data for the study is provided by Dicle (2008) based onReuters through “QuoteCenter” application of Equis International for the time period starting with January, 2000 until the end of 2007. Statistical significancelevels are indicated as “***” for 1%, “**” for 5% and “*” for 10%.



Lt−1 MRother,t−1 MRother,t−2 MRother,t−3 R2 Wald (1%) Wald (5%) Wald (10%)

−0.0008 *** −0.0950*** 0.0052 0.0291 *** 0.1389 2.97% 13.20% 21.12%−0.0008 *** 0.0370*** 0.0066 0.0083 0.1377 0.99% 4.62% 12.54%−0.0012 *** 0.0316 *** 0.0004 0.0210 *** 0.1370 2.44% 9.41% 17.07%−0.0009 *** −0.0009 −0.0270 *** 0.0238 *** 0.1409 1.98% 9.90% 17.16%−0.0009 *** −0.0041 −0.0618 *** 0.0082 0.1411 3.96% 12.54% 18.81%−0.0005 *** −0.0059 −0.0145 −0.0170 * 0.1466 1.99% 5.96% 10.93%−0.0009 *** −0.0101*** 0.0052 0.0042 0.1393 0.66% 5.94% 10.23%−0.0009 *** −0.0117 *** 0.0055 * 0.0002 0.1393 0.33% 3.96% 8.58%−0.0008 *** −0.0302 *** −0.0162 *** 0.0371 *** 0.1382 1.98% 8.91% 15.18%−0.0008 *** −0.0049 −0.0044 −0.0010 0.1615 1.66% 5.96% 9.60%−0.0008 *** −0.0048 ** −0.0028 0.0103 *** 0.1386 1.32% 6.60% 12.87%−0.0012 *** 0.0064 −0.0086 * 0.0299 *** 0.1330 0.78% 5.04% 11.24%−0.0008 *** −0.0500 *** −0.0097 0.0335 *** 0.1391 1.32% 7.59% 13.53%−0.0006 *** 0.0091 *** 0.0239 *** −0.0020 0.1501 0.33% 6.62% 12.25%−0.0008 *** −0.0206 *** −0.0111 * 0.0376 *** 0.1390 1.65% 8.91% 11.88%−0.0008 *** −0.0059 −0.0090 ** 0.0328 *** 0.1390 1.32% 6.60% 13.86%−0.0008 *** −0.0046 −0.0107 *** 0.0042 0.1398 0.66% 3.63% 7.92%−0.0008 *** −0.0064 * −0.0147 *** 0.0328 *** 0.1391 1.98% 9.24% 14.19%−0.0008 *** −0.0141 *** −0.0006 0.0163 *** 0.1389 1.32% 5.61% 10.23%−0.0008 *** −0.0209 *** 0.0022 0.0264 *** 0.1391 1.65% 8.91% 13.20%−0.0009 *** −0.0609 *** 0.0053 0.0372 *** 0.1415 5.61% 14.52% 22.44%−0.0009 *** −0.0293 *** 0.0062 * 0.0182 *** 0.1387 1.32% 8.25% 12.54%−0.0008 *** 0.0131 *** −0.0311 *** 0.0318 *** 0.1391 1.32% 5.61% 10.89%−0.0008 *** −0.0280 *** 0.0168 *** −0.0032 0.1404 2.97% 7.26% 14.19%−0.0007 *** −0.0197 *** 0.0210 *** 0.0069 0.1530 3.31% 9.93% 15.23%−0.0008 *** −0.0264 *** 0.0132 *** 0.0473 *** 0.1398 3.63% 9.57% 17.82%−0.0007 *** −0.0139 *** 0.0105 *** 0.0060 ** 0.1461 2.98% 8.28% 14.90%−0.0008 *** 0.0338 *** −0.0159 * 0.0067 0.1541 0.99% 5.30% 9.93%−0.0009 *** −0.0034 0.0077 *** 0.0131 *** 0.1383 2.31% 8.25% 13.86%−0.0008 *** −0.0006 0.0009 0.0006 0.1440 1.32% 8.25% 10.56%−0.0009 *** −0.0192 *** −0.0214 *** 0.0267 *** 0.1370 1.65% 7.92% 14.52%−0.0012 *** 0.0036 −0.0067 * 0.0228 *** 0.1405 0.69% 4.83% 10.00%−0.0009 *** −0.0537 *** 0.0311 *** 0.0304 *** 0.1374 0.66% 6.27% 10.89%−0.0008 *** −0.0048 *** −0.0035 ** −0.0048 *** 0.1362 1.32% 5.61% 11.55%−0.0008 *** −0.0079 0.0086 0.0089 0.1417 1.65% 8.25% 13.53%−0.0009 *** −0.0402 *** −0.0025 0.0459 *** 0.1404 1.98% 7.92% 18.15%−0.0009 *** −0.0070 0.0007 0.0160 *** 0.1401 0.66% 5.28% 12.21%−0.0008 *** −0.0058 −0.0021 0.0140 *** 0.1389 1.32% 5.94% 10.23%−0.0008 *** −0.0069 * −0.0214 *** 0.0240 *** 0.1382 1.32% 7.26% 13.20%−0.0008 *** −0.0107 ** 0.0042 0.0316 *** 0.1402 1.98% 6.27% 13.20%−0.0009 *** 0.0059 0.0028 0.0216 *** 0.1398 1.32% 7.59% 13.86%−0.0009 *** −0.0125 *** −0.0214 *** 0.0441 *** 0.1378 2.97% 8.58% 17.49%−0.0008 *** −0.0342*** −0.0053 0.0337 *** 0.1382 2.31% 7.59% 13.53%−0.0008 *** −0.0184 *** 0.0113 *** 0.0088 *** 0.1381 1.32% 7.92% 15.18%−0.0007 *** 0.0011 0.0135 *** 0.0139 *** 0.1471 1.32% 4.29% 8.25%−0.0008 *** 0.0087 * −0.0361 *** 0.0074 0.1381 1.65% 7.59% 16.17%−0.0009 *** 0.0028 * −0.0042 *** 0.0010 0.1374 2.97% 6.60% 12.87%−0.0008 *** −0.0001 −0.0254 *** 0.0222 *** 0.1382 0.99% 6.60% 13.20%−0.0009 *** 0.0023 −0.0249 *** 0.0098 *** 0.1377 5.28% 11.88% 21.78%



Table 12Runs test results for individual stocks traded on two Indian markets (Bombay Stock Exchange and Indian National Exchange) included in the study.

Bombay Stock Exchange
Indian Stock Exchange
Runs
Mean 1% 5% 10% Mean 1% 5% 10%
1
11.32% 18.62% 26.74% 33.72% 11.94% 10.56% 13.86% 19.14% 2 5.05% 31.42% 41.02% 47.50% 5.63% 12.21% 18.48% 22.44% 3 2.30% 29.04% 42.58% 50.37% 2.69% 11.22% 19.14% 25.74% 4 1.08% 20.43% 34.78% 44.05% 1.29% 7.59% 15.84% 23.43% 5 0.52% 10.58% 23.30% 31.26% 0.65% 3.30% 7.92% 15.51% 6 0.24% 1.72% 13.37% 22.81% 0.33% 0.99% 4.95% 10.89% 7 0.12% 0.08% 1.64% 11.57% 0.16% 0.99% 2.97% 9.24% 8 0.06% 0.41% 1.64% 3.36% 0.09% 1.32% 3.63% 5.28% 9 0.03% 1.39% 3.12% 3.86% 0.04% 1.32% 2.64% 5.28% 10 0.02% 1.64% 3.69% 3.86% 0.02% 2.64% 3.96% 4.29% 11 0.01% 1.97% 3.45% 7.79% 0.01% 0.99% 6.60% 8.91% 12 0.00% 4.68% 5.17% 5.17% 0.01% 5.61% 5.94% 5.94% 13 0.00% 3.04% 3.04% 3.04% 0.00% 2.64% 2.64% 2.64% 14 0.00% 1.89% 1.89% 1.89% 0.00% 1.32% 1.32% 1.32% 15 0.00% 2.38% 2.38% 2.38% 0.00% 1.32% 1.32% 1.32% Total 20.76% 57.83% 63.49% 67.51% 22.85% 26.73% 35.64% 41.25%
A run is defined as consecutive returns with the same sign. A runs length of two means, three consecutive trading days with the same sign. If a specific runs lengthis two, it is two consecutive runs of one, however, they are not counted towards runs of one. “Mean ” is calculated as the cross-sectional average of number of runsdivided by the number of observations for each stock. For instance, 11.32% mean for a runs length of one refers to 170 runs of one for a stock with 1500observations. The results of significance tests are provided at 1%, 5% and 10%. The percentages in corresponding columns refer to percentage of market that havestatistically significant runs test (non-random returns). Data for the study is provided by Dicle (2008) based on Reuters through “QuoteCenter” application of EquisInternational for the time period starting with January, 2000 until the end of 2007.

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Market efficiency and international diversification: Evidence from India