rakesh kumar project study

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VIKALPA • VOLUME 34 • NO 4 • OCTOBER - DECEMBER 2009 25

R E S E A R C H

includes research articles thatfocus on the analysis and

resolution of managerial andacademic issues based on

analytical and empirical orcase research

ExecutiveSummary

Asymmetric Volatility and CrossCorrelations in Stock Returns under

Risk and UncertaintyRakesh Kumar and Raj S Dhankar

KEY WORDS

Expected Volatility

Unexpected Volatility

Asymmetric Volatility

ARCH

GARCH

Global Stock Market

South Asian Stock Markets

Efficient MarketHypothesis

Capital market efficiency is a matter of great interest for policy makers and investors in de-

signing investment strategy. If efficient market hypothesis (EMH) holds true, it will prevent

the investors to realize extra return by utilizing the inherent information of stocks. They will

realize extra returns only by incorporating the extra risky stocks in their portfolios. While

empirical tests of EMH and risk-return relationship are plentiful for developed stock mar-

kets, the focus on emerging stock markets like India, Pakistan, Sri Lanka, etc., began with theliberalization of financial systems in these markets. With globalization and deregulation, the

enormous opportunities of investment in South Asian stock markets have attracted the do-

mestic and foreign institutional investors in general, and to reduce their portfolio risk by

diversifying their funds across the markets in particular.

The efforts are made in this study to examine the cross-correlation in stock returns of South

Asian stock markets, their regional integration, and interdependence on global stock market.

The study also examines the important aspects of investment strategy when investment deci-

sions are made under risk and uncertainty. The study uses Bombay stock exchange listed

index BSE 100 for India, Colombo stock exchange listed Milanka Price Index for Sri Lanka,

Karachi stock exchange listed KSE 100 for Pakistan, Dhaka stock exchange listed DSE-Gen-

eral Index for Bangladesh, and S & P Global 1200 to represent the global market. It carries out

a comprehensive analysis, tracing the autocorrelation in stock returns, cross correlations in

stock returns under risk and uncertainty, interdependency among the South Asian stock

markets, and that with the global stock market. The research methodology applied in the

study includes application of Ljung-Box to examine the cross-correlation in stock returns,

ARCH and its generalized models for the estimation of conditional and asymmetric volatilities,

and Ljung-Box as a diagnostic testing of fitted models, and finally correlation to examine the

interdependency of these markets in terms of stock returns and expected volatility. The re-

sults bring out the following:

• L-B statistics suggests the presence of autocorrelation in stock returns in all Asian stock

markets; however, for the global market, autocorrelations are significant at 15 lags, and

thereafter they are insignificant. The significant autocorrelations in stock returns report

volatility clustering in stock returns, reject the EMH, and hold that current stock returnsare significantly affected by returns being offered in the past.

• ARCH and its generalized models significantly explain the conditional volatility in all

stock markets in question.

• The study rejects the relationship between stock returns and expected volatility; however,

the relationship is significant with unexpected volatility. It brings out that investors ad-

just their risk premium for expected variations in stock prices, but they expect extra risk

premium for unexpected variations.

• With their entry into the liberalization phase, South Asian stock markets have reported

regional interdependence, and also interdependence with the global stock market.



26

An efficient capital market fully reflects the avail-

able information pertaining to stocks resulting

in investors having homogeneous expectations

of the stocks’ performance. Accordingly, investors value

the stocks taking into account the risk and return pros-

pects (Sharpe, 1964; Mossin, 1966). Such conditions pre-

vent investors from realizing abnormal returns by

utilizing the inherent information in stock prices. If effi-

cient capital market hypothesis holds true, it documents

the random walk movements in stock prices, resulting

in investors realize extra risk premium only by expos-

ing their portfolios to unexpected variations in stock

prices. Substantial empirical work supports efficient

market hypothesis in developed stock markets. The area

has great potential for research in emerging stock mar-

kets like India as well. The underlying hypothesis is that

the expected variations in stock prices (expected vola-

tility) induce the investors to adjust their risk premiumand remain invariable to these fluctuations. This study

examines this hypothesis in the South Asian context by

examining the relationship of stock returns with ex-

pected and unexpected volatility. Additionally, it inves-

tigates the regional integration among these markets and

also with the global stock market. Existing research ex-

amines the integration of stock markets by tracing the

co-movements in developed stock markets returns but

hardly any work is done in the direction of measuring

the interdependency among the South Asian stock mar-

kets. The present study makes an attempt to investigate

the regional interdependency of South Asian stock mar-

kets in terms of stock returns and volatility by examin-

ing the cross correlations in stocks returns and degree

of correlation in conditional volatilities. This line of re-

search provides the degree of regional sensitiveness of

one stock market to the ups and downs of another stock

market from the same region.

Many empirical works which investigate the seasonal

patterns in stock returns in developed and developing

stock exchanges question the efficient market hypoth-esis and suggest a seasonal pattern in these stock mar-

kets by identifying the autocorrelation in stock returns

(Aggarwal and Rivoli, 1989; Lee, 1992; Ho and Cheung,

1994; Moorkejee and Yu, 1999; Pandey, 2002; Johnson

and Soenen, 2002; 2003; Jarrett and Kyper, 2005a; 2006).

The presence of auto-correlation in time series data sig-

nifies the non-normality of the error term called hetero-

skedasticity. The existence of such phenomenon in

financial time series such as stock returns or exchange

rates exhibits volatility clustering (Karmakar, 2005; Faff

and Mckenzie, 2007; Dhankar and Charkraborty, 2007).

This suggests that large fluctuations in these series tend

to be followed by large fluctuations and small fluctua-

tions by small ones. The presence of heteroskedasticity

suggests that the past error term which represents non-

market risk or unexpected volatility affects current in-

vestment decisions. Under this situation, variance

captures aggregate fluctuations in stock returns and

thereby provides only gross volatility (Schwert, 1990;

Dhankar and Kumar, 2006; Kumar, 2007). In modeling

such phenomenon in stock returns, researchers com-

monly use autoregressive conditional heteroskedasticity

approach. Akgiray (1989), Corhay and Rad (1994) and

Brooks (1998) used US and European stock market data

and found GARCH (1,1) as better predictors of volatil-

ity. Aggarwal, Inclan and Leal (1999) examined the sud-

den change in volatility in the emerging stock marketsand found that the high volatility was attributed to a

sudden change in variance. The periods with high vola-

tility were found to be associated with important events

in each country rather than global events. In case there

is no systematic pattern, stock returns may be time vari-

ant; however, the existence of systematic variations in

the time series of stock returns suggests inefficient mar-

ket, which results in earning of extra returns not in line

with the degree of risk. It evolves the possibilities of

market manipulation wherein investors tend to earn

abnormal returns incommensurate with the degree of

risk. The present study roots its investigation back to

the study of French, Schwert and Stambaugh (1987),

wherein attempts were made to examine the relation-

ship between stock returns and expected and unexpected

volatility. Their study examined the monthly returns and

segregated monthly volatility into its expected and un-

expected components. It also estimated the relationship

between realized monthly returns and two volatility

components. They found a significant negative relation-

ship between returns and unexpected changes in vola-tility as well as a significant positive relationship between

returns and expected volatility under the GARCH-M

process. King and Wadhwani (1990), Schwert (1990),

King, and Enrique and Wadhwani, (1994) reported time

varying relationship and held that stock market returns

show high correlation during high volatility time.

Some empirical studies held monetary variables as dy-

namics of linkages between stock markets. Sasaki,

Yamaguchi and Takamasa (1999) examined the dynamic

ASYMMETRIC VOLATILITY AND CROSS CORRELATIONS IN STOCK RETURNS UNDER RISK AND UNCERTAINTY




relationship in accordance with the monetary policies

and found significant evidence to suggest that monetary

variables affected international interdependencies across

stock markets. Several studies (Hamao, Masulis and Ng,

1990; Balaban and Kan, 2001; Kumar and Mukhopadyay,

2002) employed a two-stage GARCH model to study the

dynamic relationship across the stock markets wherein

daytime and overnight returns were used. They first

extracted the unexpected shocks from the daytime re-

turns of one market and used them as a proxy for vola-

tility surprise while modeling the other markets’

overnight returns in the second stage of modeling. Fur-

ther, a number of studies (Cheung and Mak, 1992;

Karolyi and Stulz, 1996; and Masih and Masih, 2001)

employed co-integration and Granger causality tests and

held that US stock market played a dominating role in

the world stock market integration. Studies (McClure,

Clayton and Hofler, 1999;, and Hu, 2000; Frank andFrans, 2001) examining group stock markets held a

strong interdependence across the stock markets. Ewing,

Payne and Sowell (1999) examined how the North

American Foreign Trade Agreement (NAFTA) affected

the level of market integration in North America. They

however found no evidence of integration in member

markets even after NAFTA was embedded. The study

of Darrat and Zhong (2001) produced opposite results

while examining the markets of the US, Canada, and

Mexico. The results of their co-integration tests sug-

gested that NAFTA enhanced the linkages across mem-

bers’ stock markets. In conclusion, majority of the studies

found market integration to have increased significantly

over the years. Yet a number of studies questioned this

phenomenon and failed to report any dynamic relation-

ship (Cheung and Lee 1993; King, Enrique and

Wadhwani, 1994; McClure, Clayton and Hofler, 1999,

Ewing, Payne and Sowell, 1999).

DATA AND RESEARCH METHODOLOGY

This study uses market indices as the proxy for stockmarkets. The data set used in the study consists of

monthly prices of four emerging South Asian markets;

for ease of comparison with global stock market, a glo-

bal index is also used. The study uses Bombay Stock

Exchange listed index, BSE 100, for India, Colombo Stock

Exchange listed Milanka Price Index for Sri Lanka,

Karachi Stock Exchange listed KSE 100 for Pakistan,

Dhaka stock exchange listed DSE-General Index for

Bangladesh, and S & P Global 1200 to represent the glo-

bal market. Table 1 provides the details of sample indi-

ces, time period, and data source. With the given data

set, fluctuations in stock returns reflect volatility in stock

market. Suppose Pt is the price of index in time period t,

Pt-1 is the price of index in the preceding time period t-1,

the rate of return Rit investors will realize in ‘t’ time pe-

riod would be as follows:

Rt = [Loge(Pt) — Loge(Pt-1)]* 100 (1)

In fact, the realized return consists of a set of two

components—expected return E(Rt) and unexpected re-

turn ‘ε t’. While expected return is attributed to the eco-

nomic and stock fundamentals, unexpected return arises

due to good or bad news pertaining to stocks. Symboli-

cally, it can be written as follows:

Rt = E(Rt) + ε t (2)

An upswing in ε t (unexpected rise in return) suggests

the arrival of good news; on the contrary, a downswing

in ε t (unexpected decline in return) is a mark of bad news.

Volatility in stock market as a result of expected varia-

tions in stock returns is termed as expected volatility,

while volatility resultant to unexpected variations in

stock returns is known as unexpected volatility (French,

Schwert and Stambaugh, 1987). Investors and policy

makers may be interested to see the value of their port-folio in risky situations in some future point of time. In

modeling such situations, autoregressive conditional

heteroskedasticity (ARCH) approach is applied wherein

the conditional variance is used as a function of past er-

ror term and allows the variance of error term to vary

over time (Engle, 1982). It implies that volatility can be

forecasted by inclusion of the past news as a function of

conditional variance. This process is called autoregre-

ssive conditional heteroskedasticity which can be writ-

ten as follow:

22

22

2

110

2 ...................... qt pt t c −−−+++= ε α ε α ε α α σ (3)

where, α 0 > 0, α 1, α 2 .......α p ≥ 0. All things being equal, α

carries more intense influence as compared to α j. That

is, in comparison to current news, older news bears less

impact on current investment decisions which results

in volatility. Bollerslev, Chou and Kroner (1992) further

extended the ARCH process by allowing the conditional

variance to be the function of past error term as well as



28

lagged value of conditional variance. This is based on

the idea that the past error term which affects current

investment decisions and volatility in the last time pe-

riod combined together has a significant impact over the

current investment decisions. Following the introduc-

tion of ARCH models by Engle (1982) and further gen-

eralization by Bollerslev, Chou and Kroner (1992), these

models have been extensively used in explaining and

modeling the time series data of stock market. A stand-

ard GARCH (1,1) as developed by Bollerslev, Chou and

Kroner (1992), can be symbolically written as:

2

1

2

10

2

−−++= t t βσ αε α σ (4)

The magnitude and persistence of volatility in the cur-

rent time period directly depends upon the sizes of the

coefficients α i and β i. A high ‘β i’ suggests that if volatil-

ity was high yesterday, it will still be very high today.The shocks to conditional variance will take a long time

to die out. In the same fashion, the high value of ‘α i’

suggests that unexpected ups and downs in stock re-

turns react quite intensely to current market movements

resulting in spike volatility. The closer ‘α i’ is to one, the

more persistent is volatility following market shocks.

However, recent empirical studies indicate that the im-

pact of good or bad news is asymmetric on volatility

(Nelson, 1991; Chiang and Doong, 2001). That is, good

and bad news carries different magnitude of impact on

investment decisions (Bekaert and Wu, 2000). As the

GARCH model fails to take into account the asymmet-

ric effect between positive and negative stock returns,

the models such as Exponential or E-GARCH (Nelson,

1991) and Threshold Autoregressive or TAR-GARCH

(Engle and Ng, 1993; Gloston, Jagannathan and Runkle,

1993; Bae and Karoyli, 1994; and Tsay, 1998) have been

used in forecasting and estimation of volatility. These

models are used to capture the asymmetric effect of good

and bad news on investment decisions. This line of re-

search highlights the asymmetric effect of news by em-phasizing that negative shock to returns will generate

more volatility than a positive shock of equal magni-

tude. T-GARCH (1,1) model can be written as:

1

2

1

2

1

2

10

2

−−−−+++= t t t t d γε βσ αε α σ (5)

where, dt = 1 if ε t < 0, and dt = 0 otherwise.

In this model, the asymmetric volatility of index return

is captured by the estimated coefficient γ . Good news

(ε t < 0), and bad news (ε t > 0), have differential effects

on the conditional variance — good news has an impact

of α , while bad news has an impact of α + γ . If γ > 0 , we

say that the leverage effect exists. If γ ≠ 0 , the news im-

pact is asymmetric. Chiang and Doong (2001) used T-

GARCH to examine the volatility of seven Asian stock

markets and found asymmetric effect on the conditional

volatility when daily return is used. However, the study

questions this phenomenon in the case of monthly re-

turn.

Table 1: Sample and Data Source

Country Index Data Period Data Source

India BSE1001 Jan.1996-Dec.2007 Prowess,CMIE Ltd.

Sri Lanka MPI2 Jan.1995-Dec.2005 www.cse.lk

Pakistan KSE 1003 Jul.1997-Dec.2007 www.finance.yahoo.com

Bangladesh DSE-General Index4 Jan.1995-Dec.2005 www.dsebd.org

Global Market S & P Global 12005 Jun.2001-Dec.2007 www.online.wsj.com

EMPIRICAL FINDINGS

Preliminary Results

Some of the stochastic properties of the market returns

of global and South Asian markets are presented in Ta-ble 2, which highlights the distribution of risk and re-

turns in these markets for study time periods. The

positive average return of all the markets highlights the

1 BSE 100 is value weighted index, comprises 100 stocks listed withBombay stock exchange. It represents approximately 75 per centmarket capitalization.

2 MPI is one of the most quoted index in Sri Lanka stock market, repre-sents 25 stocks listed with Colombo stock exchange. It was intro-duced in January 1999, replacing the Sensitive Price Index (SPI).

3 The KSE 100 index was introduced in 1991 and comprises 100 stocksselected on the basis of sector representation and highest market capi-

talization, which captures over 80 per cent of the total market capi-talization of the companies listed on Karachi stock exchange.

4 DSE-GI which has been calculated for A, B, G, and N categories of stocks is broad based and highly quoted index of Dhaka stock ex-change.

5 The S&P Global 1200 Index is a real time, free-float weighted stockmarket index of global stocks compiled by Standard & Poor’s. Theindex covers 31 countries and approximately 70 per cent of globalmarket capitalization. It is comprised of six regional indices-S&P 500Index; S&P TSX 60 Index (Canada); S&P Latin America 40 Index(Mexico, Brazil, Argentina, Chile); S&P TOPIX 150 Index (Japan); S&PAsia 50 Index (Hong Kong, Korea, Singapore, Taiwan); S&P ASX 50Index (Australia); and S&P Europe 350 Index.




30

15 lags, after which they are insignificant, indicating that

investors have already utilized inherent information of

stocks.

Model Estimation, Forecasting of ConditionalVolatility and Diagnosis Testing

The above tests report significant non-linear dependencein the stock returns of global and South Asian markets.

The ‘L-B’ statistics which examines the autocorrelations

in stock returns for lags 1 through 25, holds volatility

clustering, i.e., serial correlation in stock returns. After

tracing this phenomenon, the next task is to fit a best

model in the global and South Asian markets’ stock re-

turns which can significantly explain the conditional

volatility in these markets. Thus, an ARCH process or

its generalized models may be best fit in explaining the

non-linear dependence as reported in stock returns of

the stock markets under consideration. To fit in the bestmodel, various criteria like Akaike information and

Schwarz criterion are used. Table 3 reports the estimated

models with their coefficients and ‘p’ values for all stock

markets. It reports that India’s conditional volatility can

be modeled with GARCH (2,0) model, where ARCH

terms up to 2 lags are significant, holding that unex-

pected fluctuations in stock prices make investors to re-

plan their investment strategy, whereas the volatility in

the preceding time period has no impact upon inves-

tors’ decisions, investors being invariable to expectedfluctuations in stock prices. This is a clear indication that

Indian stock market is moving towards efficiency. In case

of Sri Lanka and Bangladesh, GARCH (1,1) model sig-

nificantly explains the conditional volatility. Investors

in these two South Asian markets significantly redesign

their investment strategy in response to expected and

unexpected changes in stock prices due to changes in

corporate and economic factors, i.e., volatility in the pre-

ceding time period has significant impact upon the vola-

tility in the current time period. The results report

asymmetric volatility in Pakistan’s and global stockmarket (Table 3). T-GARCH (1,1) model significantly

explains the volatility in the current time period as a

function of unexpected and expected volatility in the

preceding time period. It can be observed from the re-

sults that investment decisions are certainly being im-

pacted by the good or bad news and the volatility in the

preceding period. These results question the symmet-

ric movements in stock returns and hold the rejection of

efficient market hypothesis in stock markets in question.

After fitting the models, it is important to test the best

fit of these models which can significantly explain the

conditional volatility in South Asian and global markets.

The study again applied L-B test to examine the ran-

domness of residual and squared residuals of stock re-

turns for all the stock markets in questions. If the fitted

models significantly explain the conditional volatility,

then the residuals at different lags should have zero

mean and constant variance-residuals at different lags

should be serially uncorrelated. Table 4 highlights the

computed L-B statistics of residuals from 1 through 25

lags of null hypothesis of no autocorrelation. The ‘L-B’

statistics suggesting no correlation in residuals of all

stock markets, holds that the fitted models best fit in

explaining the volatility.

Table 3: Forecasting of Volatility-Model Estimation

India α 0

α 1

α 2

GARCH (2,0) 60.23* -0.17* 0.16*(0.000) (0.003) (0.050)

Sri Lanka α 0

α 1

β 1

GARCH (1,1) 69.91* 0.07* -0.57*(0.000) (0.000) (0.000)

Pakistan α 0

α 1

β 1

λ

T-GARCH(1,1) 3.26* -0.11* 1.03* -0.11*(0.000) (0.000) (0.000) (0.000)

Bangladesh α 0

α 1

β 1

GARCH (1,1) 14.71* 0.29* 0.53*

(0.025) (0.000) (0.000)

Global Market α 0

α 1

β 1

λ

T-GARCH(1,1) 1.15* -0.23* 0.98* 0.28*(0.000) (0.019 (0.001) (0.000)

Note: * Significant at 5 % level of significance.

Relationship of Stock Returns with Expected andUnexpected Volatility

Conflicting empirical evidence is reported with regard

to relationship between stock returns and conditional

volatility, and standardized residuals (unexpected vola-tility). Studies (French, Schwert and Stambaugh, 1987;

Campbell and Hentschel, 1992) found the relation be-

tween stock returns and conditional volatility positive,

whereas a number of studies have held this relationship

as negative (Nelson, 1991; Glosten, Jagannathan and

Runkle, 1993; Bekaert and Wu, 2000; Wu, 2001). The

present study also examines the relationship of stock

returns with that of expected volatility and unexpected

volatility by estimating Equations 6 and 7 respectively.





t t VolExpR ω φ φ ++= ..10 (6)

t t t VolUnR ω φ φ ++= .exp10 (7)

The findings reported in Table 5 suggest that the rela-

tionship between stock returns and expected volatilityas measured by φ 1 is not significant in the case of all the

South Asian stock markets thereby implying no corre-

lation between the two. However, it is significant for

the global stock market as it reports a positive relation-

ship between stock returns and expected volatility.

When measuring the relationship between stock returns

and unexpected volatility, coefficient φ 1 is significant and

suggests a positive relationship between stock returns

and unexpected volatility. These results bring out the

important elements of investment strategy. Investors

adjust their risk premium in advance in view of the an-ticipated or expected variations in stock prices as a re-

sult of the ups and downs in corporate and economic

fundamentals. The direct observations can be made here

that investors do not react spontaneously to expected

variations in stock prices and they continue to hold the

same portfolios. However, the significant positive rela-

tionship between stock returns and unexpected volatil-

ity brings out the fact that investors expect risk premium

for exposing to unexpected variations in stock prices. If

efficient market holds true, they will realize higher re-

turns by bearing this risk.

Integration of South Asian Stock Markets withGlobal Stock Market

The liberalization of financial systems in the line of WTOnorms, has led the growth of South Asian stock markets

in terms of market capitalization and foreign institutional

investments. The high earning prospects of these mar-

kets have attracted foreign capital on a large scale. It is

evident from Table 2 that South Asian stock markets

have offered high mean returns to investors as compared

to the global market. During the globalization and de-

regulation regime, it has become important to examine

the responsiveness of these stock markets to their re-

gional and global trading partners. It has become an area

of interest for researchers and policy makers to examinethe dynamic linkages among the South Asian markets,

as it will facilitate the investors to reduce their portfolio

risk by achieving the optimum diversification of funds

across the markets. A number of empirical studies have

examined the integration of stock markets (Sheng and

Tu, 2000; Johnson and Soenen, 2002; Nath and Verma,

2003; Mukherjee and Mishra, 2007) and possible dynam-

ics like interest rate, foreign investment, trade relations,

and inflation which integrate the markets (Black and

Table 4: Diagnostic Testing of Fitted Models

LB statistic India Sri Lanka Pakistan Bangladesh Global

Q(5) 2.50** 1.76** 1.08** 6.98** 5.73**(0.776) (0.880) (0.955) (0.221) (0.333)

Q(10) 9.28** 7.26** 5.92** 8.21** 8.38**(0.505) (0.701) (0.821) (0.607) (0.591)

Q(15) 11.05** 13.46** 7.64** 11.44** 12.71**(0.749) (0.566) (0.937) (0.720) (0.665)

Q(20) 14.54** 14.97** 12.25** 18.35** 13.90**(0.802) (0.778) (0.907) (0.564) (0.835)

Q(25) 20.54** 16.30** 14.93** 23.51** 16.41**(0.718) (0.905) (0.943) (0.548) (0.902)

Q2 (5) 3.28** 4.16** 2.38** 0.75** 6.003**(0.656) (0.528) (0.794) (0.980) (0.305)

Q2 (10) 4.06** 7.45** 5.57** 3.68** 11.02**(0.944) (0.682) (0.850) (0.961) (0.356)

Q2 (15) 12.57** 15.09** 9.61** 6.10** 12.72**(0.635) (0.445) (0.843) (0.978) (0.623)

Q2 (20) 22.02** 18.73** 16.04** 9.99** 13.87**

(0.339) (0.539) (0.714) (0.968) (0.837)Q2 (25) 26.20** 23.98** 24.34** 12.23** 16.73**

(0.397) (0.520) (0.499) (0.985) (0.891)

Note:** Not significant at 5 % level of significance.



32

Fraser, 1995; Bracker, Docking and Koch, 1999; Bekaert,

and Harvey, 2000; Wu, 2001; Bekaert, Harvey, and

Lundblad 2001; Pretorius, 2002; Liu, Lin and Lai, 2006).

The recent liberalization of financial systems and accel-

erating trade relations, have also integrated the South

Asian stock markets. Table 6 provides the correlationmatrix of stock returns of global and South Asian mar-

kets. The results clearly report that the returns of Indian

stock market are positively correlated with global and

other South Asian stock markets. The degree of correla-

tion is very high with the global and Pakistan’s stock

markets. With India’s entry into the liberalization phase

in 1992, the Indian stock market has witnessed foreign

investment on a large scale, which has promoted its link-

ages with the other markets. To a lesser degree, it is also

correlated with the Sri Lankan stock market. The dete-

riorating trade relations of India with Bangladesh couldbe attributed to its weak correlation with the Bangla-

desh’s stock market. Although the Sri Lankan stock

market is negatively correlated with the global stock

market, the degree of the relationship is not very high.

On the other hand, the stock markets of Bangladesh and

Pakistan are positively correlated.

Table 7 exhibits the correlations of conditional volatility

of the South Asian stock markets with the global mar-

kets. A high correlation is the index of high vulnerabil-

ity of stock market to international shocks; a low corre-

lation, on the other hand, is the indication of confidence

of investors in the stock market. A high correlation

clearly indicates that investors give weightage to inter-

national shocks in their investment decisions. Good newsmotivate them to take risks in stock market resultant to

rise in the stock prices; bad news, contrarily, force them

to alter their stand in line with global developments. The

conditional volatilities of Indian, global, and other South

Asian stock markets are not along the same lines (Table

7). The low correlations of Indian stock market bring

out the fact that investors having exposure to Indian

stock market are less affected by global developments,

whereas high correlation of Pakistan and Bangladesh

stock markets’ conditional volatility suggests the sensi-

tiveness of these markets to global shocks. Table 8, onthe other hand, provides the correlation of expected

volatility among the South Asian markets and with glo-

bal market. The results reveal that Indian stock market

tend to move positively with Pakistan and Sri Lanka,

but negatively with global and Bangladesh stock mar-

kets with the emergence of expected global economic

and non-economic shocks.

Table 5: Relationship between Stock Returns and Conditional Volatility and Residuals

Relationship φ 0

φ 1

R2

India Return and expected volatility 0.04** 0.021** 0.01(0.984) (0.370)

Return and unexpected volatility 0.037** 7.89* 0.96(0.770) (0.000)

Sri Lanka Return and expected volatility -1.61** 0.02** 0.01

(0.556) (0.459)Return and unexpected volatility 0.17** 8.63* 0.97

(0.148) (0.000)

Pakistan Return and expected volatility 2.86** -0.01** 0.00(0.171) (0.459)

Return and unexpected volatility -0.18** 9.479* 0.93(0.434) (0.000)

Bangladesh Return and expected volatility 1.54** -0.01** 0.01(0.162) (0.106)

Return and unexpected volatility -0.42** 9.34* 0.80(0.300) (0.000)

Global Market Return and expected volatility 1.68* -0.104* 0.07

(0.000) (0.017)Return and unexpected volatility 1.68* -0.104* 0.07

(0.009) (0.017)

Note: * Significant at 5 % level of significance.** Not significant at 5 % level of significance.





CONCLUSION AND IMPLICATIONS

OF THE STUDY

In this paper, attempts are made to examine the cross-

correlations in stock returns, asymmetric volatility, and

relationship of stock returns with expected and unex-

pected volatility for South Asian stock markets and glo-

bal stock market. Additionally, the paper also investi-

gates regional integration in the South Asian stock mar-

kets and with the global stock market. Liberalization of

these stock markets has created enormous opportuni-

ties for investment, attracting the attention of foreign

institutional investors. The mean returns clearly indi-

cate that these markets have offered higher returns to

the investors as compared to the global stock market

(Table 2).

The Ljung-Box statistics which tests the autocorrelation

in stock returns strongly rejects the null hypothesis and

holds the presence of autocorrelations. The significant

autocorrelations question the random walk behaviour

of stock returns, suggesting that global and South Asian

stock markets are informationally inefficient. The pre-

vailing stock prices have not absorbed the historical and

available information pertinent to stocks. Inference canbe drawn here that the investors’ current investment

decisions are strongly influenced by the previous time

period decisions. These findings are consistent with that

of the previous research, which finds non-linearity and

seasonal variations in stock returns in the South Asian

stock markets. When serial autocorrelations are found

in stock returns, the use of variance as a measure of risk

provides inconsistent estimates of volatility. The study

has applied ARCH and its extension models to explain

the conditional volatility in stock returns under consid-

eration, which have been found to best fit the data ad-equately.

The study brings out important facts about the stock

returns relationship with expected and unexpected vola-

tility. It finds no relationship between stock returns and

expected volatility, suggesting that investors adjust their

risk premium in advance for the expected volatility and

that they do not alter their portfolios in response to the

expected variations in stock returns. The positive sig-

Table 6: Correlation Matrix of Stock Markets’ Returns

India Sri Lanka Pakistan Bangladesh Global

India 1.00

Sri Lanka 0.17 1.00

Pakistan 0.37 0.23 1.00

Bangladesh 0.09 -0.06 -0.02 1.00

Global 0.36 -0.05 0.03 0.16 1.00

Table 7: Correlation Matrix of Stock Markets’ Conditional Volatility


India 1.00

Sri Lanka 0.17 1.00

Pakistan 0.02 0.06 1.00

Bangladesh -0.03 -0.03 -0.11 1.00

Global -0.05 0.07 0.62 -0.28 1.00

Table 8: Correlation Matrix of Stock Markets’ Expected Volatility


India 1.00

Sri Lanka -0.17 1.00

Pakistan 0.02 0.06 1.00

Bangladesh -0.03 -0.03 -0.10 1.00

Global -0.06 0.05 0.64 -0.27 1.00



34

nificant relationship between stock returns and unex-

pected volatility, however, suggests that investors real-

ize extra risk premium for taking advantage of

unexpected variations in stock returns.

The study also finds that the liberalized trade relations

and financial systems have positively integrated the

South Asian stock markets with the global stock mar-ket. However, regional integration among the markets

is not much encouraging, which is an indication of poor

trading relations and financial flows among these mar-

kets. The results report a positive correlation of Indian

stock market with the global and other South Asian stock

markets. The degree of correlation is very high with glo-

bal and Pakistan’s stock markets. The accelerating finan-

cial flows from institutional investors have promoted

its linkages with the markets. To a lesser degree, it is

also correlated with the Sri Lankan stock market in view

of the expanding trade relations. However, India’s stock

market is weakly correlated with Bangladesh’s stock

market. The Sri Lankan stock market is negatively cor-

related with the global stock market, though the degree

of relationship is not much low. As against it, Sri Lankan

and Pakistan’s stock markets are positively correlated

to each other. These findings are important for inves-

tors and policy makers as they will facilitate them to

design investment strategy for maximizing the returns

of their portfolios by diversification among the South

Asian stock markets.

To conclude, the study reports weak interdependency

among the South Asian stock markets and also with theglobal stock market. Here, we have taken S & P Global

1200 as the benchmark of global stock market which is a

value weighted index, compiled on the basis of certain

number of indices of different leading stock exchanges.

As a matter of fact, all the South Asian markets may not

be having the same trading relations and financial flows

with these markets. The weak interdependency among

South Asian markets bring out the poor trading rela-

tions and financial flows. Though these markets have

been liberalized, the interdependency among the mar-

kets are not encouraging. However, the scope of the

study would widen by including the impact of economic

and non-economic explanatory variables on the integra-

tion of the South Asian markets. It would also provide a

better understanding of the dynamics of the linkages

over a period of time.

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Rakesh Kumar is currently an Assistant Professor in the De-partment of Business Studies, Deen Dayal Upadhyaya Col-lege (University of Delhi ), New Delhi. He has obtained hisPh.D. degree from the Faculty of Management Studies, Uni-versity of Delhi. His areas of research include risk-return rela-tionship, investment decisions under risk and uncertainty, anddeterminates of stock market volatility.

e-mail: [email protected]

Raj S Dhankar is a Professor of Finance in the Faculty of Man-agement Studies, University of Delhi (South Campus), NewDelhi, India, and is currently Visiting Professor, Faculty of Business Administration, Lakehead University, Ontario,Canada. He holds a Ph.D. and P.D.S. in the area of Finance.He earned his P.D.S. from John Anderson School of Manage-ment, UCLA, USA . He has published extensively in leadingFinance Journals. He has been the Vice-Chancellor of MaharshiDayanand University, Rohtak (Haryana) in the past.

e-mail: [email protected]


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