46

2REVIEW OF LITERATURE

In the absence of the empirical evidence, it would be difficult to know the behavior of the stock

market volatility. While empirical tests of return-volatility behavior are plentiful for developed

stock markets, the focus on developing and emerging stock markets has only begun in recent

years the interest in these emerging markets has arisen from the increased globalization and

integration of the world economy in general and that of financial markets in particular. The

globalization and integration of these markets has created enormous opportunities for domestic

and international investors to diversify their portfolios across the globe. As a result, rigorous

empirical studies examining the efficiency and other characteristics of these markets would be of

great benefit to investors and policy makers at home and abroad. In this study an effort has been

made to reevaluate the results of previous studies concerning the topic stock market volatility in

developing countries so that a realistic conclusion can be drawn.

47

The empirical studies related to the stock market volatility, reviewed in this chapter are grouped

in various categories as follows:

Studies related to measurement of stock market volatility.

Studies concerning day of the week effect on stock market return and volatility.

Studies related to relationship between stock market and macroeconomic variable.

2.1 Studies concerning Measurement of Stock Market Volatility

Number of researcher has made their contribution in the direction of measurement of stock

market volatility. A brief explanation of their research work is given below.

French et al (1987) examined the relation between stock return and stock market volatility by

using GARCH-in-mean model of Engle et al and found positive relation between expected risk

premium and volatility.

Choudhry et al. (1996) conducted a study on volatility, risk premia and the persistence of

volatility in six emerging stock markets before and after the stock market crash of 1987. The

market data were taken from Argentina, Greece, India, Mexico, Thailand and Zimbabwe. The

results show changes in the ARCH parameter, risk premia and persistence of volatility before

and after the 1987 crash. However, the changes are not uniform and depend upon the individual

markets. Furthermore, other factors may also have contributed to the changes.

48

TABLE 2.1: RESULTS OF PREVIOUS STUDIES ON THE MEASUREMENT OF

STOCK MARKET VOLATILITY.

Author Market Volatility Model Results

French et al (1987) S&P Composite

portfolio

GARCH-in-mean Positive relationship

between risk and return.

Choudhry et al.

(1996)

6 Emerging stock

market

ARCH &GARCH Change in persistence

Of volatility before and

after the 1987 crash.

De Santis and

Imrohoroglu (1997)

14 emerging stock

market

GARCH-M Positive relationship

between risk and return

but not significant.

Aggarwal, Inclan, and

Leal (1999)

10 emerging market GARCH Political, social and

economic event are main

cause of change in

volatility.

Lee et al. (2001) China stock market GARCH-M Positive relationship

between risk and return

but not significant.

Guojun Wu, (2001) US stock market Asymmetric ARCH Leverage effect is main

determinant of volatility

.

Li et al. (2003) 12 developed market GARCH-M Negative relationship

between risk &return in

most case.

Xuejing Xing, (2004) 37 International

market

GARCH Size of stock market and

education level of

investors affect volatility.

Jaeun Shin, (2005) Emerging stock

markets.

GARCH –in-mean Positive relationship

between volatility and

expected return but not

49

significant.

Hui Guo and

Christopher J. Neely,

(2006

MSCI stock market Component GARCH

& Standard GARCH

Positive risk-return trade

off.

Charlie X. Cai ,

Robert W. Faff ,

David J. Hillier and

Michael D.

McKenzie,(2006)

Emerging and

developed market

GARCH Emerging market

exposures were higher

than developed market

exposure.

Frimpong Joseph

Magnus and , Oteng-

Abayie Eric

Fosu,(2006)

Ghana stock exchange GARCH ,

EGARCH,

TGARCH

GARCH model

outperformed the other

model.

Rajni Mala and

Mahendra Reddy,

(2007)

Fiji stock market ARCH & GARCH 7 out of 16 firms listed

on Fiji were volatile.

Chiaku Chukwuogor

and Mete Feridun,

(2007)

15 emerging and

developed European

market

STANDARD

DEVIATION

Emerging market had

higher volatility and

higher return.

Sami Khedhiri and

Naeem Muhammad,

(2008)

UAE stock market ARCH & TGARCH Presence of leverage

effect.

J. Cunado, L. A. Gil-

Alana and F. Perez de

Gracia, (2008)

US stock market GARCH Volatility is more

persistent in bear market

than bull market.

Christos Floros,(2008 Egypt & Israel stock

market

GARCH, EGARCH,

TGARCH,

CGARCH,

PGARCH.

Increased risk will not

necessarily lead to a rise

in the returns.

Sabur Mollah and

Asma

Developed &

Emerging markets

GARCH Longer persistent shock

in emerging market than

50

Mobarek,(2009) in developed market.

Hung-Chun Liu, Yen-

Hsien Lee and Ming-

Chih Lee, (2009)

China stock market GARCH Volatility forecast by

GARCH-SGED is more

accurate than GARCH-

N.

Hojatallah

Goudarzi,(2011)

BSE 500 EGARCH &

GARCH

Presence of asymmetric

effect.

Amir Rafique and

Kashif-Ur-

Rehman,(2011)

Karachi stock

exchange.

ARCH & GARCH Variance structures of

high frequency data were

dissimilar from low

frequency of data.

De Santis and Imrohoroglu (1997) Investigated the risk –return relationship based on a

parametric GARCH-M model, and report positive but not statistically significant relationships

between stock market returns and conditional variance in most of the 14 emerging stock markets

under investigation.

Aggarwal, Inclan, and Leal (1999) explored the stock market volatility of 10 largest emerging

markets in Asia and Latin America. They found that shifts in volatility of considered emerging

markets is related to important country-specific political, social, and economic events. Moreover,

the time- varying stock market volatility is modelled by GARCH models.

Lee et al. (2001) investigated the risk –return relationship based on a parametric GARCH-M

model, and report positive but not statistically significant relationships between stock market

returns and conditional variance in China’s stock markets.

Robert Engle, (2001) ARCH and GARCH models have been applied to a wide range of time

series analyses, but applications in finance have been particularly successful and have been the

focus of this introduction. Financial decisions are generally based upon the tradeoff between risk

and return; the econometric analysis of risk is therefore an integral part of asset pricing, portfolio

optimization, option pricing and risk management. He has presented an example of risk

51

measurement that could be the input to a variety of economic decisions. The analysis of ARCH

and GARCH models and their many extensions provides a statistical stage on which many

theories of asset pricing and portfolio analysis can be exhibited and tested.

Guojun Wu, (2001) developed an asymmetric volatility model where dividend growth and

dividend volatility are the two state variables of the economy. The model allows the leverage

effect and the volatility feedback effect, the two popular explanations of asymmetry. The model

is estimated by the simulated method of moments. he found that both the leverage effect and

volatility feedback are important determinants of asymmetric volatility, and volatility feedback is

significant both statistically and economically.

Li et al. (2003) found that a positive but statistically insignificant relationship exists for all the 12

major developed markets. By contrast, using a flexible semiparametric GARCH-M model, they

document that a negative relationship prevails in most cases and is significant in 6 out of the 12

markets.

Xuejing Xing, (2004) there are substantial differences in stock market volatility across countries.

He examined why market volatility differs across countries. Using DataStream Country Indexes

covering thirty seven international markets, he found that the education level of investors plays a

significant role in explaining cross-country market volatility differences. In addition, there is

some evidence indicating that market industry concentration, the relative size of the stock

market, and the number of firms listed may also be of significant explanatory power to cross-

sectional market volatility differences. These findings can help predict international market

volatility.

Jaeun Shin, (2005) Both parametric and semi parametric GARCH in mean estimations found a

positive but insignificant relationship between expected stock returns and volatility in emerging

stock markets. The 1997–1998 global emerging market crises seem to induce changes in

GARCH parameters.

Hui Guo and Christopher J. Neely, (2006) they analysed the risk-return relation using the

component GARCH model and international daily MSCI stock market data. In contrast with the

52

previous evidence obtained from weekly and monthly data, daily data show that the relation is

positive in almost all markets and often statistically significant. Likelihood ratio tests reject the

standard GARCH model in favor of the component GARCH model, which strengthens the

evidence for a positive risk-return tradeoff. Consistent with U.S. evidence, the long-run

component of volatility is a more important determinant of the conditional equity premium than

the short-run component for most international markets.

Charlie X. Cai , Robert W. Faff , David J. Hillier and Michael D. McKenzie,(2006) They

empirically investigated the exposure of country-level conditional stock return volatilities to

conditional global stock return volatility. It provides evidence that conditional stock market

return volatilities have a contemporaneous association with global return volatilities. While all

the countries included in the study exhibited a significant and positive relationship to global

volatility, emerging market volatility exposures were considerably higher than developed market

exposures.

Frimpong Joseph Magnus and , Oteng-Abayie Eric Fosu,(2006) modeled and forecasted

volatility (conditional variance) on the Ghana Stock Exchange using a random walk (RW),

GARCH(1,1), EGARCH(1,1), and TGARCH(1,1) models. The unique ‘three days a week’

Databank Stock Index (DSI) was used to study the dynamics of the Ghana stock market volatility

over a 10-year period. The competing volatility models were estimated and their specification

and forecast performance compared with each other, using AIC and LL information criteria and

BDS nonlinearity diagnostic checks. The DSI exhibits the stylized characteristics such as

volatility clustering, leptokurtosis and asymmetry effects associated with stock market returns on

more advanced stock markets. The random walk hypothesis was rejected for the DSI. Overall,

the GARCH (1,1) model outperformed the other models under the assumption that the

innovations follow a normal distribution.

Rajni Mala and Mahendra Reddy, (2007) Volatility of returns in financial markets can be a

major stumbling block for attracting investment in small developing economies. In this study,

they used the Autoregressive Conditional Heteroskedasticity (ARCH) models and its extension,

the Generalized ARCH model was used to find out the presence of the stock market volatility on

Fiji’s stock market. The analysis was done using a time series data for the period 2001-2005 on

53

specific firms and it was found out that seven out of the sixteen firms listed on Fiji’s stock

market is volatile. The volatility of stock returns were then regressed against the interest rates

and the results showed that the interest rates changes have a significant effect on stock market

volatility. Using a priori theory and knowledge, the impact of factors like government

regulations, low levels of liquidity on volatility were also derived.

Chiaku Chukwuogor and Mete Feridun, (2007) this paper examines the volatility of returns in

fifteen emerging and developed European stock markets. A set of parametric and non-parametric

tests is used to test the equality of mean returns and standard deviations of the returns. Results

suggest that there was generally high volatility of returns in the markets during the period1997-

2004 and that there were some surprises in terms of volatility and loss of value in the case of

some developed European stock markets. The emerging markets in general had higher returns

and higher volatilities, particularly Russia and Turkey. Even though the markets of Russia,

Turkey and Spain showed the highest standard deviations, the markets that displayed the highest

coefficients of variation are those of Austria, Belgium, Czech Republic, Denmark, France,

Germany, Italy, Switzerland and Turkey. The results of the Levene’s(1960) could not reject the

Null Hypothesis that means returns are equal across the days of the week for all the markets

except for Italy.

Ahmed Shamiri, Zaidi Isa and Abu Hassan, (2008) Being able to choose most suitable

volatility model and distribution specification is a more demanding task. They introduced an

analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool

to evaluate and compare the predictive abilities of possibly misspecified density forecast models.

The main advantage of this statistical tool is that they used the censored likelihood functions to

compute the tail minimum of the KLIC, to compare the performance of a density forecast models

in the tails. They included an illustrative empirical application to compare a set of distributions,

including symmetric/asymmetric distribution, and a family of GARCH volatility models. They

highlighted the use of our approach to a daily index, the Kuala Lumpur Composite index

(KLCI). results shows that the choice of the conditional distribution appear to be a more

dominant factor in determining the adequacy of density forecasts than the choice of volatility

model. Furthermore, the results support the Skewed for KLCI return distribution.

54

Sami Khedhiri and Naeem Muhammad, (2008) Financial market volatility of developed

economies have been studied extensively since the 1987 stock market crash as well as the

volatility of the East Asian stock markets after the East Asian financial crisis. However the

volatility characteristics of the financial markets in the Middle East are far from being

thoroughly analyzed despite their tremendous growth in recent years. The purpose of this study

was twofold. First, they investigated the volatility characteristics of the UAE stock markets

measured by fat tail, volatility clustering, and leverage effects, in order to explore a parsimonious

model for the UAE stock market and predict its future performance. Second, they used switching

regime ARCH methodology to assess the impact of stock market openness to foreign investors

on the market returns and they analyze its observed irregular performance using recently

developed methodologies. The change in the volatility pattern and the recent irregular behavior

of the stock market came as a result of the introduction of a new regulation allowing foreign

investors to participate in the UAE stock markets. , they identified a significant leverage effect

such that a stock price decrease would have a greater impact on subsequent volatility than a stock

price increase with the same magnitude.

J. Cunado, L. A. Gil-Alana and F. Perez de Gracia, (2008) they tested whether the stock market

volatility presents a different behavior in bull and bear phases. Using long range dependence

techniques they estimated the order of integration in the squared returns in the US stock market

(S&P 500) over the sample period August, 1928 to December, 2006. The results suggest that

squared returns present long memory behavior. In general, the estimates of d are above 0 and

below 0.5 implying long memory stationarity for the volatility processes. The results also show

that in many cases the volatility is more persistent in the bear market than in the bull market.

Christos Floros, (2008) examined the use of GARCH-type models for modelling volatility and

explaining financial market risk. he used daily data from Egypt (CMA General index) and Israel

(TASE-100 index). Various time series methods were employed, including the simple GARCH

model, as well as exponential GARCH, threshold GARCH, asymmetric component GARCH, the

component GARCH and the power GARCH model. he found strong evidence that daily returns

can be characterized by the above models. For both markets, he concluded that increased risk

55

will not necessarily lead to a rise in the returns. The most volatile series is CMA index from

Egypt, because of the uncertainty in prices (and economy) over the examined period. These

findings were strongly recommended to financial managers and modelers dealing with

international markets.

Sabur Mollah and Asma Mobarek,(2009) investigated the time-varying risk return relationship

and the persistence of shocks to volatility within GARCH framework both in developed and

emerging markets. Found that there is a long-term persistence shock in emerging markets

compared to developed markets.

Hung-Chun Liu, Yen-Hsien Lee and Ming-Chih Lee, (2009) investigated how specification of

return distribution influences the performance of volatility forecasting using two GARCH

models (GARCH-N and GARCHSGED).Daily spot prices on the Shanghai and Shenzhen

composite stock indices provided the empirical sample for discussing and comparing the relative

out-of-sample volatility predictive ability, given the growth potential of stock markets in China

in the eyes of global investors. Empirical results indicated that the GARCH-SGED model is

superior to the GARCH-N model in forecasting China stock markets volatility, for all forecast

horizons when model selection is based on MSE or MAE. Meanwhile, the DM-tests further

confirmed that volatility forecasts by the GARCH-SGED model are more accurate than those

generated using the GARCH-N model in all cases, indicating the significance of both skewness

and tail-thickness in the conditional distribution of returns, especially for the

emerging financial markets.

.

Hojatallah Goudarzi,(2011) studied the effects of good and bad news on volatility in the Indian

stock markets using asymmetric ARCH models during the global financial crisis of 2008-09. The

BSE500 stock index was used as a proxy to the Indian stock market to study the asymmetric

volatility over 10 year’s period. Two commonly used asymmetric volatility models i.e.

EGARCH and TGARCH models were used. The BSE500 returns series found to react to the

good and bad news asymmetrically. The presence of the leverage effect would imply that the

negative innovation (news) has a greater impact on volatility than a positive innovation (news).

This stylized fact indicates that the sign of the innovation has a significant influence on the

56

volatility of returns and the arrival of bad news in the market would result in the volatility to

increase more than good news. Therefore, he concluded that, bad news in the Indian stock

market increases volatility more than good news.

Amir Rafique and Kashif-Ur-Rehman,(2011) compared the variance structure of high (daily)

and low (weekly, monthly) frequencies of data. By employing ARCH (1) and GARCH (1, 1)

models, they found that the intensity of the shocks was not equal for all the series and statistical

properties of the three data series of returns were substantially different from one another and the

persistence of conditional volatility was also different for the three series. The presence of

persistency was more in the daily stock returns as compared to other data sets, which showed

that the volatility models were sensitive to the frequencies of data series. In simple, the results

revealed that the variance structure of high-frequency data were dissimilar from the low

frequencies of data, and variance structure in the daily data were more linked with the stylized

facts associated with stock returns volatility as compared to other data series.

2.2 Studies Concerning Day of the Week Effect on Stock Market Return

and Volatility.Various study have been carried out on the topic day of the week effect on stock market return

and volatility, summary of some of the study are mentioned below.

Rogalski, J. Richard (1984) they discovered that all of the average negative returns from Friday

close to Monday close documented in the literature for stock market indexes occurs during the

non-trading period from Friday close to Monday open. In addition, average trading day returns

(open to close) are identical for all days of the week. January/firm size/turn-of-the-year

anomalies are shown to be interrelated with day-of-the-week returns."

Jeffrey Jaffe and Westerfield Randolp (1985) they examined the daily stock market returns for

four foreign countries. They found a so-called “week-end effect” in each country. In addition, the

lowest mean returns for the Japanese and Australian stock markets occur on Tuesday.

57

Josef Lakonishok and Smidt Seymour (1988) They studied 90 years of daily data on the Dow

Jones Industrial Average to test for the existence of persistent seasonal patterns in the rates of

return. Methodological issues regarding seasonality tests are considered. They found evidence of

persistently anomalous returns around the turn of the week, around the turn of the month, around

the turn of the year, and around holidays."

Anup Agrawal and Kishore Tandon, (1994) they examined five seasonal patterns in stock

markets of eighteen countries: the weekend, turn-of-the-month, end-of-December, monthly and

Friday-the-thirteenth effects. They found a daily seasonal in nearly all the countries, but a

weekend effect in only nine countries. Interestingly, the daily seasonal largely disappears in the

1980s. The last trading day of the month has large returns and low variance in most countries.

Many countries have large December pre-holiday and inter-holiday returns. The January returns

are large in most countries and a significant monthly seasonal exists in ten countries."

TABLE 2.2: RESULTS OF PREVIOUS STUDIES ON THE TOPIC DAY OF THE WEEK

EFFECT ON STOCK MARKET VOLATILITY AND RETURN

Author Market Effect on

VolatilityEffect on Return

Rogalski, J. Richard (1984) Developed Market ------------Monday and

Friday

Jeffrey Jaffe and Westerfield

Randolph (1985) Four Developed Market ------------ Friday

Anup Agrawal and Kishore

Tandon, (1994)18 Countries ------------

Friday in 9

countries

M.Dubois and P. Louvet

(1996) Nine Countries ------------Lower at

beginning

Sunil Poshakwale, (1996) BSE ------------ Friday

Ravindra R. Kamath, Rinjai

Chakornpipat, and ArjunThailand Market(SET) ------------

Monday and

Friday

58

Chatrath, (1998)

Choudhary (2000) 7 Asian Emerging

MarketPresent Present

Hakan Berument and Halo

Kiymaz (2001)S&P 500

Friday and

Wednesday

Monday and

Wednesday

Halil Kiymaz, Hakan

Berument (2003) Developed Market Present Present

Hassan Aly, Seyed Mehdian,

and Mark J. Perry (2004) Egyptian stock Market ------------ No effect

Harvinder Kaur 2004 BSE & NSE Wednesday Wednesday

Chiaku Chukwuogor, 200615 Emerging and

Developed Market------------

Monday in

Developed and

Wednesday in

emerging

Mahendra Raj and Damini

Kumari(2006) BSE & NSE ------------

Positive Monday

and Negative

Tuesday

Ankur Singhal and Vikram

Bahure(2006) Indian Stock Market ------------Monday and

Friday

Syed A. Basher and Perry

Sadorsky(2006) 21 Emerging MarketNo effect in most

of the countries------------

Chander, Ramesh / Mehta,

Kiran/Sharma, Renuka/2008

BSE(SENSEX),BSE(100

),S&P CNX Nifty, S&P

CNX 500

------------Monday and

Friday

U.S. Agathee (2008) Mauritius Market ------------ Friday

Md. Lutfur Rahman, ( 2009) Dhaka Stock Exchange ------------ Thursday

Aboudou Maman Tachiwou ( West African Regional ------------ Tuesday and

59

2010) Stock Mrket Friday

Ricky Chee-Jiun Chia and

Venus Khim-Sen Liew,(2010) BSE ------------Monday and

Friday

Padhi Puja, (2010) BSE & NSE Friday Friday

Eleftherios Giovanis (2010) 55 Stock Market ------------

Mixed Result in

Different

Countries

M.Dubois and P. Louvet (1996) They examined the day-of-the-week effect for eleven indexes

from nine countries during the 1969–1992 period. The standard methodology as well as the

moving average methodology are used and they found returns to be lower at the beginning of the

week (but not necessarily on Monday) for the full period.

Sunil Poshakwale, (1996) Stock market efficiency is an important concept, for understanding

the working of the capital markets particularly in emerging stock market such as India. The

efficiency of the emerging markets assumes greater importance as the trend of investments is

accelerating in these markets as a result of regulatory reforms and removal of other barriers for

the international equity investments. There is enough evidence on market efficiency and day of

the week effect in the developed markets, however, the same is not true for the emerging stock

markets. He found empirical evidence on weak form efficiency and the day of the week effect in

Bombay Stock Exchange over a period of 1987-1994. The results provide evidence of day of the

week effect and that the stock market is not weak form efficient. The day of the week effect

observed on the BSE pose interesting buy and hold strategy issues.

Ravindra R. Kamath, Rinjai Chakornpipat, and Arjun Chatrath,( 1998). They examined the

day-of the-week effect in the Securities Exchange of Thailand using OLS as well as GARCH

models. They examined the aggregate stock index, SET, as well as its ten industry-classified

indices over a 15-year period starting in 1980. They found persisting day-of-the-week effects

irrespective of the methodology employed. The findings are in direct contrast with earlier

60

suggestions that the day-of-the-week anomalies are exaggerated by traditional treatments to the

data.

Choudhary (2000) Investigated the day of the week effect on seven emerging Asian stock

markets returns and conditional variance (volatility). The empirical research was conducted

using the GARCH model and daily returns from India, Indonesia, Malaysia, Philippines, South

Korea, Taiwan, and Thailand from January 1990 to June 1995. Results obtained indicate the

significant presence of the day of the week effect on both stock returns and volatility, though the

result involving both the return and volatility are not identical in all seven cases. Results also

show that these effects may be due to a possible spill-over from the Japanese stock market

Hakan Berument and Halo Kiymaz (2001) They tested the presence of the day of the week

effect on stock market volatility by using the S&P 500 market index during the period of January

1973 and October 1997. The findings show that the day of the week effect is present in both

volatility and return equations. While the highest and lowest returns are observed on Wednesday

and Monday, the highest and the lowest volatility are observed on Friday and Wednesday,

respectively. Further investigation of sub-periods reinforces their findings that the volatility

pattern across the days of the week is statistically different.

Halil Kiymaz, Hakan Berument (2003) Investigated the day of the week effect on the volatility

of major stock market indexes for the period of 1988 through 2002. Using a conditional variance

framework, they found that the day of the week effect is present in both return and volatility

equations. The highest volatility occurs on Mondays for Germany and Japan, on Fridays for

Canada and the United States, and on Thursdays for the United Kingdom. For most of the

markets, the days with the highest volatility also coincide with that market’s lowest trading

volume. Thus, they supports the argument made by Foster and Viswanathan [Rev. Financ. Stud.

3 (1990) 593] that high volatility would be accompanied by low trading volume because of the

unwillingness of liquidity traders to trade in periods of high stock market volatility.

Hassan Aly, Seyed Mehdian, and Mark J. Perry (2004) They investigated daily stock market

anomalies in the Egyptian stock market using its major stock index, the Capital Market Authority

Index (CMA), to shed some light on the degree of market efficiency in an emerging capital

61

market with a four-day trading week. The results indicate that Monday returns in the Egyptian

stock market are positive and significant on average, but are not significantly different from

returns of the rest of the week. Thus, no evidence was uncovered to support any daily seasonal

patterns in the Egyptian stock market, indicating that stock market returns are consistent with the

weak form of market efficiency. These results should be interpreted with caution since the

Egyptian stock market has only a limited number of stocks that are actively traded.

Harvinder Kaur (2004) Investigated the nature and characteristics of stock market volatility in

India. The volatility in the Indian stock market exhibits characteristics similar to those found

earlier in many of the major developed and emerging stock markets. Various volatility estimators

and diagnostic tests indicate volatility clustering, i.e., shocks to the volatility process persist and

the response to news arrival is asymmetrical, meaning that the impact of good and bad news is

not the same. Suitable volatility forecast models are used for Sensex and Nifty returns to show

that:The ‘day-of-the-week effect’ or the ‘weekend effect’ and the ‘January effect’ are not present

while the return and volatility do show intra-week and intra-year seasonality.

The return and volatility on various weekdays have somewhat changed after the

introduction of rolling settlements in December 1999.

There is mixed evidence of return and volatility spillover between the US and Indian

markets.

The empirical findings would be useful to investors, stock exchange administrators and policy

makers as these provide evidence of time varying nature of stock market volatility in India.

Specifically, they need to consider the following findings of the study:

For both the indices, among the months, February exhibits highest volatility and

corresponding highest return. The month of March also exhibits significantly higher

volatility but the magnitude is lesser as compared to February. This implies that, during

these two months, the conditional volatility tends to increase. This phenomenon could be

attributed to probably the most significant economic event of the year, viz., presentation

of the Union Budget. The investors, therefore, should keep away from the market during

March after having booked profits in February itself. The surveillance regime at the stock

exchanges around the Budget should be stricter to keep excessive volatility under check.

62

Similarly, the month of December gives high positive returns without high volatility and,

therefore, offers good opportunity to the investors to make safe returns on Sensex and

Nifty. On the contrary, in the month of September, i.e., the time when the third quarter

corporate results are announced, volatility is higher but corresponding returns are lower.

The situation is, therefore, not conducive to investors.

The ‘weekend effect’ or the ‘Monday effect’ is not present. For other weekdays, the

‘higher (lower) the risk, higher (lower) the return’ dictum does not hold consistently and

Wednesday provides higher returns with lower volatility making it a good day to invest.

The domestic investors and the stock exchange administrators do not need to lose sleep

over gyrations in the major US markets since there is no conclusive evidence of

consistent relationship between the US and the domestic markets.

The volatility forecast models presented for Sensex and Nifty can be used to forecast

future volatility of these indices.

Chiaku Chukwuogor, (2006) Examined the financial markets’ trends such as the annual returns,

daily returns and volatility of returns in 15 emerging and developed European financial markets.

A set of parametric and non-parametric tests is used to test the equality of mean returns and

standard deviations of the returns. Although positive annual index closing price changes were the

norm between 1997 and 2004, many of the European indexes experienced negative changes

especially in 1998 and 2002. It is important to note that between 1999 and 2000, the Russian

MTM and the Turkish XU, 100 achieved astronomical growth. There was presence of the day of

the week effect during the period 1007-2004. Seven of the European Financial markets

experienced negative returns on Monday and seven others also experience negative returns on

Wednesday. There was generally high volatility of returns in the European markets. The results

of the Levene’s (1960) test of the equality of standard deviations of the returns at the 5 percent

confidence level could not reject the Null Hypothesis that mean returns are equal across the days

of the week for all the markets except for MBTEL, Italy.

Mahendra Raj and Damini Kumari(2006) investigated the presence of seasonal effects in the

Indian stock market. They tested Week day effects, day-of-the-week, weekend, January and

April effects by applying a variety of statistical techniques. The results are interesting and

63

contradict some of the findings found elsewhere. The negative Monday effect and the positive

January effects were not found in India. Instead the Monday returns are positive while Tuesday

returns are negative. The seasonal effects in the Indian market have been examined by the two

major indices, the Bombay Stock Exchange Index and the National Stock Exchange Index.

However, it must be remembered that the Indian economy became deregulated from 1991 and

this may have had an impact on the markets.

Ankur Singhal and Vikram Bahure(2006) Many studies on the behavior of stock prices have

been based on the belief that stock returns are not influenced by the day of the week. In this

paper, they have argued that the measured daily returns should depend on the day of the week by

taking the context of the Indian stock market. More specifically, they believed that the expected

returns on Monday should be lower and returns on Friday should be higher than on other days by

evidencing the existence of this 'weekend effect'. they have also offered a partial explanation to

this anomalous behavior by considering a model for adjusted stock returns based on the delay

between the trading and settlement period, complex effects of holidays on daily returns and

effect of investor expectations.

Syed A. Basher and Perry Sadorsky(2006) used both unconditional and conditional risk analysis to

investigate the day-of-the-week effect in 21 emerging stock markets. In addition, risk is allowed

to vary across the days of the week. Different models produce different results but overall day-

of-the-week effects are present for the Philippines, Pakistan and Taiwan even after adjusting for

market risk. The results in this study show that while the day-of-the-week effect is not present in

the majority of emerging stock markets studied, some emerging stock markets do exhibit strong

day-of-the-week effects even after accounting for conditional market risk.

Chander, Ramesh / Mehta, Kiran/Sharma, Renuka/(2008) In informationally efficient markets,

investors and analysts are not likely to predict stock price movements consistently. Still, market

participants make concerted efforts to earn abnormal returns discerning some anomalous pattern

in the stock price movements. They empirically scrutinizes whether this pattern yields abnormal

return consistently for any specific day of the week. Four market series, namely, the BSE

Sensex, BSE 100, S&P CNX Nifty, and S&P CNX 500 were considered on a daily basis for a

10-year period. The entire series is divided into two sub-periods, viz., (1) pre-rolling settlement

period, April 1997-December 2001; and (2) post-rolling settlement period, January 2002- March

64

2007. Contrary to the earlier findings, they documents the lowest Friday returns on the BSE in

the pre-rolling settlement period. The findings recorded for post- rolling settlement period were

in harmony for those obtained elsewhere in the sense that Friday returns were the highest and

those on Monday were the lowest to document credible evidence for the day-of-the-week effect.

It may be inferred that the arbitrage opportunities existed have not only subsided consequent to

the introduction of the compulsory rolling settlement but also the pattern of market movements

have become even more akin to that experienced in the developed capital markets. On the whole,

they found the presence of the day-of-the-week effect in the Indian stock markets.

U.S. Agathee (2008) investigated the day of the week effects in an emerging market, inparticular

the Stock Exchange of Mauritius, using observations as from the year the SEM started its

operation on a daily basis for a full calendar year to 2006. The study shows that the Friday

returns appeared to be higher relative to other trading days. However, on overall, further

empirical results suggest that the mean returns across the five week days are jointly not

significantly different from zero across all given years as well as for the whole sample period of

1998-2006.

Md. Lutfur Rahman,( 2009) Examined the presence of day of the week effect anomaly in Dhaka

Stock Exchange (DSE). Several hypotheses have been formulated; dummy variable regression

and the GARCH (1, 1) model were used in the study. The result indicates that Sunday and

Monday returns are negative and only positive returns on Thursdays are statistically significant.

Result also reveals that the mean daily returns between two consecutive days differ significantly

for the pairs Monday-Tuesday, Wednesday-Thursday and Thursday-Sunday. Result also shows

that the average daily return of every working day of the week is not statistically equal. Dummy

variable regression result shows that only Thursdays have positive and statistically significant

coefficients. Results of GARCH (1, 1) model show statistically significant negative coefficients

for Sunday and Monday and statistically significant positive coefficient for Thursday dummies.

The conclusion of all the findings is that significant day of the week effect present in DSE.

Aboudou Maman Tachiwou (2010) Found the first evidence for the presence of the day of the

week effects in West African regional stock market in the sample for the period September 1998

to December 2007.The observed daily patterns exhibiting lower daily means and lower standard

65

deviations. In local currency terms, a pattern of lower returns around the middle of the week,

Tuesday and then Wednesday; and a higher pattern towards the end of the week, Thursday and

then Friday, are observed. The results have useful implications for international portfolio

diversification. This may be of particular interest for the global investor.

Ricky Chee-Jiun Chia and Venus Khim-Sen Liew,(2010) Examined the existence of day-of-the-

week effect and asymmetrical market behavior in the Bombay Stock Exchange (BSE) over the

pre-9/11 and post-9/11 sub-periods. They found the existence of significant positive Monday

effect and negative Friday effect during the pre-9/11 sub-period. Further analysis using the

EGARCH and EGARCH-M models revealed the asymmetrical market reaction to the positive

and negative news in BSE. Moreover, significant day-of-the-week effect is found present in BSE

regardless of sub-periods, after controlling for time-varying variance and asymmetrical market

behavior.

Padhi Puja, (2010) The average return on Friday is known to be high and for Monday less,

which is termed as "days-of the-' week effect" or "week-end" effect. she checked whether there is

the presence of the days-of-the week effect in the aggregate indices including Sensex and Nifty,

BSE 100, BSE 500 and S&P CNX 500 by modeling linear regression, GARCH (1,1),GARCH-M

(1,1) and asymmetric model EGARCH and GJR model. The linear regression shows the days of

the week effect in the Sensex. In the GARCH (1,1) model Nifty shows the days-of-the-week

effect. All other indices are showing statistically insignificant results. The risk factor is positive

for Nifty and BSE100. "

Eleftherios Giovani (2010) studied the well known day of the week effect in stock returns.

Specifically, fifty five stock market indices from fifty one countries are examined with

asymmetric GARCH models. The results are mixed, as the Monday effect is reported in nine

indices, while in other ten indices Friday presents the highest positive returns where Monday

returns are not the lowest or are statistically insignificant. Furthermore Wednesday and Thursday

present the highest returns in nine and eleven stock indices respectively. On the contrary a

reverse Monday effect pattern is reported in twelve indices, indicating that there is a shift in the

day of the week effect. Finally, only in two stock markets, which are examined, returns in all

trading weekdays are statistically insignificant. The study of this paper is not restricted in

66

regional or national level but is extended in global level. The purpose of this research study is to

provide and capture the different daily patterns formulated among the stock markets. The

identification of these patterns indicates that the market efficiency hypothesis is violated, and

provides information to fund managers and financial traders with result the optimal allocation of

their portfolio and the maximization of profits.

2.3 Studies Concerning Impact of Macroeconomic Variable on Stock Market

Return and Its Volatility

It is widely believed that stock market price is related to macroeconomic fundamentals. The

relation between the stock market price and macroeconomic forces has been widely analyzed in

finance and macroeconomic literature. summary of some previous studies on this topic are given

below.

Muzafar Shah Habibulah, Ahmad Zubaidi Baharumshah (1996) determined whether

macroeconomic variables, in particular money supply and output are important in predicting

stock prices in Malaysia. Monthly data on stock price indices, money supply and output were

employed in this study. The stock price indexes used in this study are Composite, Industrial,

Finance, Property, Plantation and Tin. For money supply they used both M1 and M2, and output

is measured by real Gross Domestic Product (GDP). Our results suggest that Malaysia’s stock

market is informationally efficient with respect to money supply as well as output.

TABLE 2.3: RESULTS OF PREVIOUS STUDIES ON THE TOPIC IMPACT OF

MACROECONOMIC VARIABLE ON STOCK MARKET RETURN AND ITS

VOLATILITY.

Author Market Tools used Results

Muzafar Shah

Habibulah, Ahmad

Zubaidi

Baharumshah

Malaysian Market VECM Stock market is

informationally

efficient with respect

to money supply &

67

(1996) output

Ramin Cooper

Maysami, Lee

Chuin Howe and

Mohamad Atkin

Hamzah (2004)

Singapore stock

market

Johansen’VECM Long term

relationship with

macroeconomic

variable.

L.M.C.S.

Menike(2006)

Sri Lankan stock

market

Multivariate

Regression

Macroeconomic

variable affect equity

market.

Md. Nehal Ahmed

and Mahmood

Osman Imam

(2007)

Bangladesh stock

market

Co-integration test

&VECM

Stock prices are not

co integrated with

macroeconomic

variable.

Shefali Sharma and

Balwinder

Singh(2007)

BSE ARIMA Macroeconomic

variable affect stock

market in post reform

era.

Agrawalla Raman

K., Senior

Economist, Tuteja S.

K.(2008)

Indian stock market VECM Causal relationship

was found between

macroeconomic

variable and stock

market.

Lekshmi R.

Nair(2008)

Indian stock market Johansen’s co-

integration

Some variable affect

stock market

development.

Ming-hua liu,

Keshab M.

Shrestha(2008)

China stock market Heteroscedastic co-

integration

Stock market

performance is

positively related to

macroeconomic

variable.

Jaafar Pyeman and Malaysian stock Co-integration Change in macro

68

Ismail

Ahmad(2009)

market variable lead to

change in some of the

indices.

Aisyah Abdul

Rahman, Noor

Zahirah Mohd

Sidek and Fauziah

Hanim Tafri (2009)

Malaysian market VAR Market is sensitive to

change in the

macroeconomic

variable.

Ajay kumar

chauhan and Ashish

garg(2010)

S&P CNX Nifty VAR FII engaged in

positive feedback

trading while Mutual

fund engaged in

negative feedback

trading.

George Filis(2010) Greek market Co-

integration,VECM,

VAR

No relationship

between industrial

production and stock

prices.

Xiufang Wang

(2010)

China stock market VAR & EGARCH Market is less

efficient and

somewhat separated

from real economy.

Gagan Deep

Sharma and

Mandeep

Mahendru (2010)

BSE Multiple Regression Exchange rate & gold

prices highly affect

stock prices but

influence of inflation

is less.

Imran Ali, Kashif

Ur Rehman, Ayse

Kucuk Yilmaz,

Muhammad Aslam

Karachi stock

exchange

Johansen’Co-

integration & Granger

Causality test

No causal relationship

was found between

macroeconomic

indicator and stock

69

Khan and Hasan

Afzal(2010)

prices.

T.O. Asaolu and

M.S.

Ogunmuyiwa(2011)

Nigerian stock market Johansen’Co-

integration & Granger

Causality test, ECM

A weak relationship

exist between

macroeconomic

variable and stock

prices.

Karam Pal, Ruhee

Mittal, (2011)

BSE, S&P CNX Co-integration and

ECM

Capital market indices

are dependent on

macroeconomic

variable.

Ramin Cooper Maysami, Lee Chuin Howe and Mohamad Atkin Hamzah (2004) examined the

long-term equilibrium relationships between selected macroeconomic variables and the

Singapore stock market index (STI), as well as with various Singapore Exchange Sector

indices—the finance index, the property index, and the hotel index. They concluded that the

Singapore’s stock market and the property index form cointegrating relationship with changes in

the short and long-term interest rates, industrial production,price levels, exchange rate and

money supply. Implications of the study and suggestions for future research are provided.

L.M.C.S. Menike (2006) investigated the effects of macroeconomic variables on stock prices in

emerging Sri Lankan stock market using monthly data for the period from September 1991 to

December 2002. The multivariate regression was run using eight macroeconomic variables for

each individual stock. The null hypothesis which states that money supply, exchange rate,

inflation rate and interest rate variables collectively do not accord any impact on equity prices is

rejected at 0.05 level of significance in all stocks.The results indicate that most of the companies

report a higher R2 which justifies higher explanatory power of macroeconomic variables in

explaining stock prices. Consistent with similar results of the developed as well as emerging

market studies, inflation rate and exchange rate react mainly negatively to stock prices in the

Colombo Stock Exchange (CSE). The negative effect of Treasury bill rate implies that whenever

70

the interest rate on Treasury securities rise, investors tend to switch out of stocks causing stock

prices to fall. However, lagged money supply variables do not appear to have a strong prediction

of movements of stock prices while stocks do not provide effective hedge against inflation

specially in Manufacturing, Trading and Diversified sectors in the CSE. These findings hold

practical implications for policy makers, stock market regulators, investors and stock market

analysts.

Md. Nehal Ahmed and Mahmood Osman Imam (2007) investigated whether current economic

activities in Bangladesh can explain stock market returns in long-run horizon by using co

integration test and in short-run dynamic adjustment from a vector error correction model. In

addition, this paper tests causality of economic variables on stock returns and vice-versa.This

paper fond that the Bangladesh stock market does not reflect macroeconomic effect on stock

price indices. The co integrationtest and the vector error correction model illustrate that stock

priceindices are not co integrated with a set of macroeconomic variables like industrial

production index, broad money supply and GDP growth. Findings of no co-integration between

the growth of stock market return and fundamental macroeconomic factors may be the outcome

of a small and shallow emerging stock market of Bangladesh. But interest rate change or T-bill

growth rate may have some influence on the market return. the findings that change of interest

rate Granger causes stock market returns unidirectionally implies that stock market index is not a

leading indicator for the economic variable of the change in interest rate,which shows the

evidence of informationally inefficient market.

Shefali Sharma and Balwinder Singh(2007) analysed the relationship between stock prices and

macroeconomic variables with implications on efficiency of stock markets. The analysis is based

on monthly data from April 1986 to March 2005 on foreign exchange reserves, claims on private

sector, whole sale price index, call money rate, index of industrial production, exchange rate and

broad money. Using time-series data, the analysis has undergone several preliminary statistical

tests viz. unit root, autoregressive integrated moving average (ARIMA) model testing etc. The

analysis reveal the relative influence of these macroeconomic variables on the Sensitive Index of

the Bombay Stock Exchange. Although there have been some periods of fluctuations, certain

variables like foreign exchange reserves, exchange rate, index of industrial production, money

71

supply (M3) and claims on private sector have considerable influence on the stock market

movement. The study finally confirms the traditional belief that the real economic variables

continue to affect the stock market in the post – reform era in India and also highlights the

insignificance of certain variables with respect to stock market.

Agrawalla Raman K., Senior Economist, Tuteja S. K.(2008) examined the causal relationships

between the share price index and industrial production for India in a multivariate vector error

correction model which involves certain other crucial macroeconomic variables namely money

supply, credit to the private sector, exchange rate, wholesale price index, and money market rate

for the reason of right and robust model specification. The purpose is to highlight the relationship

between economic growth and stock market especially in terms of stock prices. They reported

causality running from economic growth proxied by industrial production to share price index

and not the other way round.

Lekshmi R. Nair (2008) examined the macroeconomic determinants of stock market

development in India over 1993-94 to 2006-07empirically.Cointegration and error correction

modeling was used for the analysis. The results show that there is long run relationship between

all the macroeconomic variables used and stock market development. Variables like real income

and its growth rate, interest rate and financial intermediary development significantly affect

stock market development in the short run. Financial intermediary development and stock market

development are obtained to be complements in the Indian context.The variables exchange rate,

inflation and Foreign Institutional Investment (FII) have no significant influence on stock market

development in India. These findings have important implications for the policy makers as stock

markets are obtained to have a crucial role in promoting economic growth.

Ming-hua liu, Keshab M. Shrestha(2008) investigated the relationship between the Chinese

stock market indices and a set of macro-economic variables, i.e. money supply, industrial

production, inflation, exchange rate and interest rates using heteroscedastic cointegration

analysis. Results show that the cointegrating relationship does exist between stock prices and the

macro-economic variables in the highly speculative Chinese stock market. Detailed analysis

shows stock market performance is positively related to that of macro-economy in the long term.

72

Jaafar Pyeman and Ismail Ahmad(2009) analyzed the dynamic properties of the relationship

between sector-specific indices of Bursa Malaysia and macroeconomic variations. The sectoral

indices of Bursa Malaysia selected for this study are namely, Construction, Plantation, Consumer

Product, Finance, Industrial Product,Mining, Hotel, Property and Trading and Services. The

macroeconomic variables were represented by real economic activity, interest rate, inflation rate,

money supply and exchange rate. The monthly data series of the macroeconomic variables and

stock market indices are obtained for the period from 1993 to 2006. This study has identified

various trends of responses among the sector-specific indices towards the innovation in

macroeconomic variables. The results suggest that unanticipated changes in macroeconomic

variables could lead to similar patterns in some of the sector-specific indices with the effects

differing mainly in terms of speed of adjustments towards equilibrium level in the long-run.

Sulaiman D. Mohammad, Adnan Hussain, M. Anwar Jalil, Adnan Ali (2009) explored the

correlation among the macroeconomics variables and share prices of KSE (Karachi Stock

Exchange) in context of Pakistan. The study considered several quarterly data for different

macroeconomics variables are as foreign exchange reserve, foreign exchange rate, industrial

production index (IPI), whole sale price index (WPI), gross fixed capital formation (GFCF) and

broad money M2. These variables were obtained from the period 1986-2008. The result shows

that after the reforms in 1991 the influence of foreign exchange rate and foreign exchange

reserve significantly affect the stock prices, while other variables like IPI and GFCF are

insignificantly affect stock prices. The result also highlighted the internal factors of firm like

increase in production and capital formation insignificant while external factor like M2 and

foreign exchange affect positively. The study will be very helpful for national policy makers,

researchers and corporate managers.

Aisyah Abdul Rahman, Noor Zahirah Mohd Sidek and Fauziah Hanim Tafri (2009) They

explored the interactions between selected macroeconomic variables and stock prices for the

case of Malaysia in a VAR framework. Some conventional econometric techniques are applied

along with a battery of complementary tests to trace out both short and long run dynamics. Upon

testing a vector error correction model, changes in Malaysian stock market index do perform a

73

co-integrating relationship with changes in money supply, interest rate, exchange rate,

reserves and industrial production index. Lag exclusion test shows that all six variables

contribute significantly to the co-integrating relationship. This shows that the Malaysian stock

market is sensitive to changes in the macroeconomic variables. Furthermore, based on the

variance decomposition analysis, this paper highlights that Malaysian stock market has

stronger dynamic interaction with reserves and industrial production index as compared to

money supply, interest rate, and exchange rate.

Ajay kumar chauhan and Ashish garg(2010) investigated the presence of feedback trading

behavior, if any with reference to foreign institutional investors (FIIs) and mutual fund

investments in relation with the benchmark market index CNX SandP Nifty. The study used the

daily data for the period started from April 1st, 2001 to December 31st,2009 and applied various

econometric models to identify the investment behavior of various institutional investors. They

concluded that the FII’s are engaged in the positive feedback trading activities and inducing

volatility in Indian stock arket, whereas the local mutual funds are found to be involved in

negative feedback trading in Indian stock market, which provide support to the market when it

becomes volatile.

George Filis(2010) examined the relationship among consumer price index, industrial

production, stock market and oil prices in Greece. Initially a unified statistical framework

(cointegration and VECM) was used to study the data in levels, then employed a multivariate

VAR model to examine the relationship among the cyclical components of our series. The period

of the study is from 1996:1 to 2008:6. Findings suggest that oil prices and the stock market

exercise a positive effect on the Greek CPI, in the long run. Cyclical components analysis

suggests that oil prices exercise significant negative influence to the stock market. In addition, oil

prices are negatively influencing CPI, at a significant level. However, he found no effect of oil

prices on industrial production and CPI. Finally, no relationship can be documented between the

industrial production and stock market for the Greek market. The findings of this study are of

particular interest and importance to policy makers, financial managers, financial analysts and

investors dealing with the Greek economy and the Greek stock market

74

Xiufang Wang (2010) investigated the time-series relationship between stock market volatility

and macroeconomic variable volatility for China using exponential generalized autoregressive

conditional heteroskedasticity (EGARCH) and lag-augmented VAR (LA-VAR) models. They

found evidence that there is a bilateral relationship between inflation and stock prices, while a

unidirectional relationship exists between the interest rate and stock prices, with the direction

from stock prices to the interest rate. However, a significant relationship between stock prices

and real GDP was not found. His results suggest that China’s stock market is likely to be less

efficient than those in the U.S. and other developed countries and is somewhat separated from

the real economy of China.

Gagan Deep Sharma and Mandeep Mahendru (2010) analyzed the long term relationship

between BSE and macro economic variables vis-à-vis change in exchange rate, foreign exchange

reserve inflation rate and gold rate. The multiple regression model was used in order to

investigate the relationship among these factors. The period of the study was January 2008 to

January 2009. The results reveal exchange rate and gold price highly effects the stock prices

whereas the influence of inflation rate and foreign exchange reserve was limited.

Imran Ali, Kashif Ur Rehman, Ayse Kucuk Yilmaz, Muhammad Aslam Khan and Hasan

Afzal(2010) examined the causal relationship between macro-economic indicators and stock

market prices in Pakistan. The data from June 1990 to December 2008 have been used to analyze

the causal relationship between various macro-economic variables and stock exchange prices.

The set of macro-economic indicators includes; inflation, exchange rate, balances of trade and

index of industrial production, whereas the stock exchange prices have been represented by the

general price index of the Karachi Stock Exchange, which is the largest stock exchange in

Pakistan. The statistical techniques used include unit root Augmented Dickey Fuller test,

Johansen’s co-integration and Granger’s causality test. The study found co-integration between

industrial production index and stock exchange prices. However, no causal relationship was

found between macro-economic indicators and stock exchange prices in Pakistan. Which means

performance of macro-economic indicators cannot be used to predict stock prices; moreover

stock prices in Pakistan do not reflect the macro-economic condition of the country.

75

T.O. Asaolu and M.S. Ogunmuyiwa(2011) investigated the impact of macroeconomic variables

on Average Share Price (ASP) and goes further to determine whether changes in macroeconomic

variables explain movements in stock prices in Nigeria. Various econometric analysis such as

Augmented Dickey Fuller (ADF) test, Granger Causality test, Co-integration and Error

Correction Method (ECM) were employed on time series data from 1986-2007 and the results

revealed that a weak relationship exists between ASP and macroeconomic variables in Nigeria.

The findings further point that ASP is not a leading indicator of macroeconomic performance in

Nigeria, albeit, a long run relationship was found between ASP and macroeconomic variables for

the period under review.

Karam Pal, Ruhee Mittal, (2011) examined the long-run relationship between the Indian capital

markets and key macroeconomic variables such as interest rates, inflation rate, exchange rates

and gross domestic savings (GDS) of Indian economy. Quarterly time series data spanning the

period from January 1995 to December 2008 has been used. The unit root test, the co-integration

test and error correction mechanism (ECM) have been applied to derive the long run and short-

term statistical dynamics. They found that there is co-integration between macroeconomic

variables and Indian stock indices which is indicative of a long-run relationship. The ECM

shows that the rate of inflation has a significant impact on both the BSE Sensex and the S&P

CNX Nifty. Interest rates on the other hand, have a significant impact on S&P CNX Nifty only.

However, in case of foreign exchange rate, significant impact is seen only on BSE Sensex. The

changing GDS is observed as insignificantly associated with both the BSE Sensex and the S&P

CNX Nifty. The paper, on the whole, conclusively establishes that the capital markets indices are

dependent on macroeconomic variables even though the same may not be statistically significant

in all the cases.

After going through the above mentioned existing literature on the topic, we came to know some

shortcomings of them. First the period of the study was relatively shorter; secondly sample of the

study was limited to some countries which have been selected randomly. Thirdly the multiple

regression models was applied without verifying the properties of the time series data such as

stationary, lastly limited macroeconomic variable have been taken to test the relationship

between stock market and macroeconomic variable. The present study is an improvement over

76

the earlier studies in several ways. It has used longer period of data, in this study BRICM (Brazil,

Russia, India, China, and Mexico) were selected as sample countries, it would study all the

aspect of the stock market volatility like day of the week effect, relationship with

macroeconomic variable and its measurement.

77

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