relationship between conditional volatility of domestic macroeconomic factors and conditional stock...

Asia-Pacific Finan MarketsDOI 10.1007/s10690-014-9194-7

Relationship Between Conditional Volatility of DomesticMacroeconomic Factors and Conditional Stock MarketVolatility: Some Further Evidence from India

Jyoti Kumari · Jitendra Mahakud

© Springer Japan 2014

Abstract The present paper empirically examines the theoretical linkage betweenstock market volatility and macroeconomic volatility in emerging Indian stock marketcovering the data period from July 1996 to March 2013. Unlike the previous studies,the present study investigates the issue with two stage estimation techniques. Condi-tional volatility is extracted by employing univariate autoregressive conditional het-eroskedasticity models. Further, multivariate VAR model along with impulse responsefunction, block exogeneity and variance decomposition are carried out to analyze therelationship between stock market volatility and macroeconomic volatility. Data onmacroeconomic variables namely output, foreign institutional investments, exchangerate, short term and long-term interest rates, broad money supply, inflation and stockmarket indices BSE Sensex and NSE Nifty are used for analysis. The findings suggesta linkage between macroeconomic volatility and equity market volatility.

Keywords Autoregressive conditional heteroskedastic (ARCH) models · Condi-tional stock market volatility · Macroeconomic fundamentals · Vector autoregressivemodel (VAR)

JEL Classification C2 · C3 · C58 · E5

1 Introduction

Since the time of financial liberalization and international financial market integrationin late 1990s, emerging stock markets (ESMs) have been the large source of equity

J. Kumari (B) · J. MahakudDepartment of Humanities and Social Science (HSS), Indian Institute of Technology (IIT) Kharagpur,Kharagpur 721302, West Bengal, Indiae-mail: [email protected]; [email protected]

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J. Kumari, J. Mahakud

investments for investors across the globe. The rapid growth of ESMs, large capitalinflows and outflows, and macroeconomic conditions make the ESMs highly volatilecompared to the developed markets (Bekaert and Harvey 1997). The literature onmeasuring and forecasting the stock market volatility has been quite extensive.1 Overthe years, there is extensive discussion in the finance literature that emerging anddeveloped stock markets are sensitive to the macroeconomic news and market partici-pants tend to follow closely the relevance of any announcement of policy changes andeconomic data. Schwert (1989) in his classic paper asserts that “if macroeconomicvariables provide information about the volatility of future expected cash flows andfuture discount rates, then macroeconomic fundamentals are able to explain the stockmarket volatility”.

From the theoretical perspective, the dividend discount model (DDM) and arbitragepricing theory (APT) provide the theoretical framework through which the behaviorof macroeconomic fundamentals can be linked to the stock market volatility (see Chenet al. 1986). These models emphasize that any expected or unexpected arrival of newinformation and policy decisions regarding macroeconomic variables such as grossdomestic product (GDP), interest rates, exchange rates and foreign institutional invest-ments (FIIs), money supply and inflation will change the equity prices and further thevolatility of stocks via change in the future cash flows and expected dividends. Intu-itively, the essence of the theoretical link between the macroeconomic fundamentalsand equity market volatility is that any change or shock in the macroeconomic vari-ables will raise the source of systematic and idiosyncratic risk of the market portfolio,irrespective of how well the portfolio is diversified (Chowdhury and Rahman 2004).

Undoubtedly, the theoretical linkage between the stock market volatility and macro-economic variables is of greater importance to the policy makers and market partic-ipants. The knowledge of the stock market volatility and macroeconomic volatilityprovides insightful ideas and enables investors to formulate profit maximizing invest-ment and hedging strategies. Such knowledge also helps the policy makers to formulatethe macroeconomic policies that ensure the financial and macroeconomic stability.

The empirical literature on macroeconomic volatility and stock market volatility isof mixed nature. The early work relating to stock market volatility dates back to Officer(1973) which evidently asserts the relationship between the stock market volatility withbusiness cycle fluctuations is reflected by the variability of industrial production. In thesimilar path, Black (1976) and Christie (1982) probe the theoretical linkage betweenstock market volatility and financial leverage and find that the variance of equityreturns is related to several explanatory factors. Further, Christie (1982) advocates thatequity variance has a strong positive association with financial leverage and interestrates. Similarly, Schwert (1989) finds that macroeconomic variables play a significantrole in prediction of stock market volatility and their impact has been more during theperiod of depression. Considering the APT model, Koutoulas and Kryzanowski (1996)investigate the explanatory role of several macroeconomic variables in determiningCanadian stock market volatility and find that the macroeconomic factors i.e. industrialproduction, exchange rate, residual market factors have time varying and priced risk

1 For detail see, Poon and Granger (2003).

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Some Further Evidence from India

premium. Lettau and Ludvigson (2005), Marquering and Verbeek (2005) conclude thatindustrial production, interest rates and inflation play a consistent role in the predictionof volatility.

The further existing empirical studies focus on how volatility of macroeconomicvariables is linked to the volatility of stock market. These studies based on two step esti-mation. The first stage calculates the time varying conditional variance of the macro-economic fundamentals and stock returns. In second stage, the relationship betweenvolatility of macroeconomic fundamentals and the volatility of stock returns is ana-lyzed. Liljeblom and Stenius (1997) analyze the time varying conditional stock marketvolatility and macroeconomic volatility with the data for Finland during 1920–1991.Employing the GARCH and vector auto regression (VAR) methods, the authors inter-estingly find that one-sixth to more than two-thirds of the change in the stock marketvolatility is related to the change in the volatility of macroeconomic fundamentals suchas inflation, industrial production and money supply. In similar fashion for Australianmarket, Kearney and Daly (1998) conclude that the conditional volatility of inflationand interest rates have a larger impact on volatility of stock returns whereas, industrialproduction and money supply have indirect effects. Kearney (2000) examines the con-ditional stock market volatility in the Irish stock market from July 1975 to June 1994by employing the conditional volatility models. The findings suggest that exchangerate volatility is significant determinant of stock returns volatility than interest ratevolatility. Using the VAR framework, Morelli (2002) empirically tests the issue in theUK stock market and document no significant explanatory power of macroeconomicvolatility in determining the stock market volatility. Interestingly, Diebold and Yilmaz(2007) empirically investigate the issue taking sample of 45 markets including devel-oped and emerging and suggests a significant positive relationship between volatilityof stock returns and GDP volatility.

In multivariate framework, Sadorsky (2003) and Paye (2006) investigates the impactof macroeconomic factors on stock market volatility and find that the conditionalvolatility of term premium and consumer price index have significant impact onthe conditional volatility of technology stock prices. Extending the work of Morelli(2002), Chinzara (2011) examines the relationship between conditional macroeco-nomic volatility with stock market volatility including the sectorial indices for SouthAfrica. Unlike the past studies, this study further extends the role of financial cri-sis in influencing the relationship between macroeconomic fundamentals and stockmarket volatility. The findings indicate that volatility in both the stock markets andmacroeconomic fundamentals significantly increased with the financial crisis.

The literature on the relationship between macroeconomic factors and stock returnsvolatility in India largely emphasizes on the long run causal links and long run co-movements of the variables. For instance, Darrat and Mukharjee (1987) and Mukharjeeand Naka (1995) examine the long run relationships and co-movements of the macro-economic fundamentals and stock returns. The study has demonstrated the absence ofthe long run co-movements among the variables. However, Naka et al. (1998) find along run relationship among the variables. Panda and Kamaiah (2001) further estimatethe causal and dynamic linkages among the monetary policy variables and volatilityand conclude that macroeconomic factors cause the volatility in the market.

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From the forgoing discussion on literature, it is inferred that although there issizable literature available in both developed and emerging economies on the rela-tionship between stock market volatility and volatility of macroeconomic variables,a fresh study on the behavior of stock market volatility is required for India in num-ber of ways. First, available studies on Indian stock market are mostly focused onexamining the relationship between macroeconomic fundamentals and stock marketreturns. There are no study which examines the link between time varying conditionalvolatility of macroeconomic factors and time varying conditional volatility of stockreturns. Second, since the liberalization process in early 1990s, Indian capital markethas witnessed massive economic and structural changes. The stock market is growingsignificantly in terms of share in trade, market capitalization, trading volume, prof-itability, turnover and number of transactions. It is therefore, reasonable to expectthat ESMs like India exhibit different features and characteristics which distinguishit from developed stock markets and other ESMs. The existence of such characteris-tics influences investors, policy makers and fund managers to analyze the theoreticaland empirical linkage between macroeconomic volatility and stock market volatility.Third, in post liberalization era, the share of foreign institutional investors (FIIs) hasincreased in Indian capital market, which makes the market more volatile as the FIIsare highly yield sensitive and quickly react to the economic news and policy mea-sures. Hence, it is essential to analyze the inter linkage between variation in FIIs andmarket volatility. Fourth, in a broad package of liberalization, the interest rates werederegulated and banking sector has undergone drastic changes. Additionally, the priceinstability, high current account deficit (CAD), policy measures such as change inrepo rate (RR), cash reserve ratio (CRR) etc. lead to changes in the macroeconomicfundamentals like growth rate of gross domestic product (GDP), inflation rate, andmarket interest rates. Therefore, from the policy perspective, the present study pro-vides insights and inputs for the formulation of monetary and fiscal policies to stabilizethe capital markets.

In light of the significance of the issue, the present study has fourfold contributionsto the existing literature. First, it considers the 17 years of longer and comprehensivedata period which captures the market dynamics, policy reforms including the finan-cial liberalization and changes in market microstructure. Moreover, the study uses themonthly data, which better represents the inter linkages and responses of macroeco-nomic factors as a source of systematic risk to the stock market volatility. Second,during the study period, it is logical to expect structural breaks in macroeconomic andstock index series because of business cycles and financial crisis. Ignoring such breaksleads to incorrect inferences. The present study addresses the structural breaks issue.Third, the previous studies overlooked FIIs as one of the variables, which may affectthe stock market volatility. Considering the importance of FIIs in Indian capital marketafter liberalization, the study incorporates FII as one of the macroeconomic variables.Fourth, the previous empirical studies largely use the bivariate VAR for analyzingthe relationship between stock return volatility and macroeconomic volatility. To testthe simultaneous relationship between the macroeconomic fundamentals like output,exchange rate, foreign institutional investors, short term and long-term interest rates,money supply, inflation and volatility of the stock markets we employ multivariateVAR framework to capture the dynamics. The present study therefore, is first of its

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kind in India. The rest of the paper is structured as follows. Section 2 describes themethodology and Sect. 3 presents the data and variables. The discussion on empiricalresults is presented in Sect. 4 and the last section concludes.

2 Methodology

The present study considers the univariate non-linear conditional heteroskedastic mod-els for the estimation of monthly time varying conditional volatility for Indian stockmarket portfolios and macroeconomic fundamentals. The study employs Bollerslev’s(1986) generalized autoregressive conditional heteroskedastic models (GARCH), pop-ularly known to capture the volatility clustering and volatility symmetry effect inthe conditional variance equation. Further, Nelson’s (1991) exponential GARCH andthreshold GARCH models of Zakoian (1994) and Glosten et al. (1993) employed tocapture asymmetry effect and positive and negative news effect. The prerequisite forunivariate nonlinear models is to check for the persistence of serial correlation andthe break in the time series stock returns and conditional variance of the series. Fordiagnostics of serial correlation, we used ARCH-LM of Engle (1982) and McLeodand Li (1983) test before and after the estimation of conditional models. To identifythe trend break in the returns and variance series, Iterative Cumulative Sum Square(ICSS) break test of Inclan and Tiao (2002) is carried out.

2.1 Univariate Time Varying Conditional Volatility Models

In the present study, the comprehensive GARCH classes of models have beenemployed to extract the conditional volatility from macroeconomic fundamentals andstock returns for the second step estimation. GARCH model is most preferred modelto capture the volatility symmetry in financial returns. The GARCH (1,1) process withconditional normal distribution is the most popular generalized ARCH specificationin the empirical research. The model assumes weights on past squared residuals todecline geometrically at a rate to be estimated from the data.

yt = α + β0 Xt + εt (1)

εt/� ∼ i id(0, ht )

ht = ω +q∑

j=1

α jε2t− j +

p∑

j=1

βi ht−1 (2)

Here ω0 > 0 and αi + βi < 1.Yt represents the index stock returns, ht is conditional variance, β0 represents the

coefficient of the model. αi is the coefficients of the lagged squared residuals andβi is the lagged conditional variance. Nelson (1991) identified that the limitations ofGARCH in analyzing the financial market volatility. To overcome the weaknesses ofthe GARCH, Nelson (1991) proposed exponential GARCH model. EGARCH modelallows for asymmetric effects between positive and negative asset returns. The asym-metric property indicates that a negative shock has a bigger realization on conditional

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volatility than a positive constraint imposed by the linear GARCH models, and thus,better captures the asymmetric property of conditional volatility.

Rt = α + β0 Xt + εt (3)

εt/� ∼ i id(0, ht )

log(ht )=ω+q∑

j=1

α j

[∣∣∣∣∣εt− j√ht− j

−E

(εt− j√ht− j

)∣∣∣∣∣

]+

m∑

k=1

δkεt−k√ht−k

+p∑

i=1

βi ht−i

(4)

where ω0 > 0, αi +βi < 1, δk < 0, if volatility is asymmetric. Further, Rt representsthe Sensex and Nifty returns, log(ht ) log of conditional variance of stock returns, βthe vector of coefficient, εt white noise term, δi is asymmetric coefficient. The logof conditional variance makes the leverage effect exponential instead of quadratic,and therefore, the estimates of the conditional variance are guaranteed to be non-negative. Leverage effect is shown by δk < 0 if the impact of news is asymmetric.EGARCH model is highly significant in determining the effect of volatility magnitude,persistence of the volatility in the market and leverage effect.

To capture the negative and positive asymmetry in terms of negative and positiveshocks, TGARCH model of Zakoian (1994) and Glosten et al. (1993) is carried outin the present study. In TGARCH model, the positive and negative shocks of equalmagnitude have a different impact on stock market volatility, which is attributed asthe leverage effect (Black 1976). Similarly, negative shocks provide higher volatilitythan positive shocks of the same magnitude (Engle and Ng 1993). The main strengthof the TGARCH model is to capture the asymmetry in terms of negative and positiveshocks and adds multiplicative dummy variable to check whether there is a statisticallysignificant difference when shocks are negative. The specification for TGARCH is

Rt = α + β0 Xt + εt (5)

εt/� ∼ i id(0, ht )

ht = ω +p∑

i=1

αiε2t−1 +

q∑

j=1

β j ht− j + γ ε2t−1dt−1 (6)

where dt = 1 if εt > 0, and otherwise. In this model, good news (εt > 0) and bad news(εt < 0) have different effects on the conditional variance. Good news has an impactof α, while bad news has an impact of (α + γ ). If γ = 0, the volatility is symmetricand if γ �= 0, the volatility is asymmetric. This model is used to prove the results ofthe EGARCH model on the impact of news on the stock returns volatility.

2.2 Vector Auto Regressions (VAR) Model

Vector auto regressions (VAR) developed by Sims (1980) is a dynamic model to under-stand the relationship among the economic variables. A VAR is a systems regressionmodel where there is more than one dependent variable and the relationship is free

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from prior restriction of simultaneous equation models. The VAR system enables theestimation of a reduced form of correctly specified actual economic structure whichmay be unknown. According to Sims (1980), if there is simultaneity among a numberof variables, then there should be no distinction between exogenous and endogenousvariables. Therefore, once this distinction is abandoned, all variables are treated asendogenous. Hence, in its general reduced form, each equation has the same set ofregressors which lead to the development of the VAR models. In our study, we usemultivariate 4th order VAR model to know the relationship between the conditionalmacroeconomic volatility and stock market volatility. The multivariate VAR modelemployed in this present study is consistent with Abugri (2008).

hst = α0 +4∑

i=1

λhst−i +4∑

i=1

δhmv j t−i + εt (7)

hmv j t = ω0 +4∑

i=1

θhmv j t−i +4∑

i=1

ψhst−i + εt (8)

where hst is the conditional stock market volatility at time t, hmv j t−i is the conditionalvolatility in the macroeconomic variables j at time t − i whereas i = 1, 2, 3 . . . . is theorder of lag length. λ and ψ are the coefficient of the lagged values of the conditionalvolatility of stock returns and δ and θ coefficient of the lagged values of the macroeco-nomic volatility. This determines whether stock market volatility and macroeconomicvolatility is linked together and any spontaneous change in macroeconomic condi-tional volatility affects the volatility of stock market. Moreover, it explains the abilityof conditional stock market volatility as a determinant of macroeconomic conditionalvolatility.

Though the VAR model is useful to analyze the economic relationship among vari-ables, the model suffers from some fundamental weakness. It is a theoretical in natureand the large number of parameters involved makes the estimated model difficult tointerpret. In the model, some lagged variables may have coefficients, which changesign across the lags, and this change together with the interconnectivity of the equa-tions, could render it difficult to see what effect a given change in a variable wouldhave upon the future values of the variables in the system. In order to alleviate thisweakness, a set of statistics are generally constructed for an estimated VAR model.Hence, we use the VAR model along with the impulse response function, block sig-nificance test, and variance decompositions. The impulse response function traces outthe responsiveness of the dependent variables in the VAR to shocks of each of thevariables. So, for each variables from each equation separately, a unit shock is appliedto the error and the effects on the VAR system over time are noted. In the present study,the impulse response function traces out the response of the equity market volatility toa one error shock in any of the conditional macroeconomic volatility. In our analysis,we capture the sign, magnitude and persistence of responses of one market to shocksin another market. In this respect, the Cholesky impulse response function is usedas this method does not require orthogonalisation of innovations and does not varywith the ordering of variables in the VAR unlike other methods. The block exogeneity

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significance test restricts all of the lags of particular variables to zero. In the presentstudy, we use the block significance tests to identify the particular macroeconomicvariables whose volatility significantly influences the stock market volatility and viceversa.

The variance decomposition offers a slightly different method for examining theVAR system dynamics. Variance decomposition allows the proportion of the move-ments in the dependent variables not only due to their own shocks, but also to theshocks in other variables. Accordingly, a shock to the i th variable will directly affectsthat variable, but it will also be transmitted to all of the other variables in the systemthrough the dynamic structure of the VAR. Variance decomposition in our analysisexplains how much the forecast error variance of a conditional stock market volatil-ity is explained by the innovations to each explanatory conditional macroeconomicvolatility. In practice, it can be usually observed that future conditional stock marketvolatility will be explained by the own shocks rather than the innovations and shocksfrom macroeconomic variables.

3 Data and Variables

The present study uses the monthly data on stock indices and macroeconomic vari-ables from July 1996 to March 2013. For stock market returns, we choose the monthlyclosing prices of BSE Sensex and NSE Nifty, the major indices traded in Indian stockmarket. The NSE and BSE constitute 99 percent of total market capitalization. In thisstudy, a comprehensive set of macroeconomic variables like short term and long terminterest rates, rupee dollar exchange rate (EX), foreign institutional investments (FIIs),output (IIP), broad money supply (M3), and Inflation (WPI) are used for the empir-ical analysis.2 Theoretically, asset prices are influenced by the systematic economicnews, which includes the non-diversifiable risk. The economic theories of interestrate attributes that change in interest rates have a substantial impact on the value ofthe firms through impact on cash flows, cost of capital, investments and profitability.Moreover, the unanticipated changes in the interest rate will therefore, influence priceof the stock, through its influence on the time value of future cash flows, which furtherinfluences stock returns volatility (Chen et al. 1986). In this study, both the short termand long term nominal interest rates are considered for the analysis. We use the 91days Treasury Bill Rate (TBR) as short term interest rate and 10 years bond yield aslong term interest rate.

There are two theoretical approaches, which explain the linkage between exchangerate and stock returns. First, the “flow oriented approach” (Dornbush and Fisher 1980)and second the “stock oriented approach” (Branson 1983; Frankel 1983). The floworiented models claim positive linkage between EX rate and stock returns via thecountry’s current account balance or trade balance. The flow oriented model assumesthat changes in EX rates affect the international competitiveness and trade balance

2 Chen et al. (1986) and Chen (1991) note that economic theory provides a theoretical linkage betweeneconomic fundamentals, stock returns and volatility of the stock returns. The rationale behind the selectionof macroeconomic fundamentals is generally based on the similar intuition.

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and consequently affects the income and output. Depreciation in domestic currencyoffers opportunities for domestic firms given that their exports become cheaper ininternational trade. The surge in exports will increase the domestic income and hencethe firms’ stock prices will appreciate since they are evaluated at the present valueof the firms’ future cash-flows. From stock oriented approach side, the EX rate isdetermined by the demand and supply of financial assets. We have two types of stockoriented models namely the portfolio balance and the monetary balance. Both theseapproaches claim the negative linkage between stock prices and EX. More precisely,the portfolio balance model considers an internationally diversified portfolios and thefunction of EX rate movements in balancing the demand and supply of domestic andforeign financial assets. In this process, an increase in domestic stock price returns willproduce an appreciation of the domestic currency and domestic stock price increasewill encourage the international investors to revise their portfolio selection. Specif-ically, they will jointly buy more domestic assets and sell foreign ones in order tohave domestic currency available for buying new domestic assets and consequently,domestic currency will depreciate. Therefore, the rupee-dollar EX is used consideringthe importance of dollar as a major currency in India’s trade and investment.

FII raises the liquidity in the ESMs, which stimulates the arbitrage activity andhence, the faster capitalization of information in the stock prices and smaller depar-tures from a random walk benchmark (Chordia et al. 2008). With the emergence ofportfolio management in investment decisions, FIIs capital flows to India has rapidlyincreased after 1990s. During 1992–1993 transactions by FIIs in terms of cumulativenet investment in India was US$ 1,638 million, which substantially increased to US$1,70,989 million during 2012–2013 (SEBI 2013). Therefore, with the increased role ofFIIs in Indian stock market, we add FIIs as a macroeconomic variable in the analysis.The corporate capital structure and future cash flows of a firm are directly related tothe output or gross domestic product (GDP) of the economy as a whole or industrialproduction (Fama 1990; Ferson and Harvey 1998). Ross (1986) suggests index ofindustrial production as an economic activity indicator. Considering its importance,we incorporate IIP as proxy for output. According to quantity theory of money, thesupply of money determines the long term price level in the economy. Any change inits supply creates proportionate change in price level positively and negative change invalue of the money via change in the volatility of expected future cash flows and creditsupply by the monetary aggregates in the economy (Friedman and Schwartz 1970).Therefore, we choose broad money supply (M3) as a predominant macroeconomicvariable.

Fisher (1930) theorizes the relationship between stock returns and inflation. Fisherstates that stock returns are hedge against inflation. As the general price level rises,investors are fully compensated for the rising price level by a corresponding increase inthe nominal stock returns, leaving real returns unaffected. Fisher’s (1930) hypothesisadvocates that real stock returns should be independent of inflation. In contrast toFisher hypothesis, Fama’s (1981) proxy hypothesis claims a negative relationshipbetween stock returns and inflation. Fama critically explains high inflation reducesthe economic activity and further explained by the combination of demand for moneyand the quantity theory of money. In light of its theoretical and policy relevance, weuse inflation calculated from wholesale price index (WPI).

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The stock market returns are calculated as Rmt = ln(Pt )− ln(Pt−1), where Rmt isthe stock market returns for month t, ln is the logarithm, and Pt is the stock marketprices at the end of the month t and Pt−1 is the stock market price at the previousmonth t − 1. The same logarithm transformation is applied to all macroeconomicfundamentals in order to transform them into growth rates. For a uniform base year,the index number splicing method is used to shift the base year to 1994–1995 for thevariables. The data on stock indices is collected from the official websites of the NSEand BSE. The data on macroeconomic variables are obtained from the Handbook ofStatistics on Indian Economy published by the Reserve Bank of India (RBI).

4 Empirical Results and Discussion

This section presents the discussion on empirical results. The descriptive statistics pre-sented in panel A of Table 1 shows a wide difference between the low and high valuesof market returns and the mean returns of two indices are same (0.008). The skewness

Table 1 Descriptive statistics and correlation matrix

Nifty Sensex Bondyield EX FII IIP M3 Treasury Inflation

Panel A Descriptive statistics

Mean 0.008 0.008 −0.002 0.002 0.015 0.005 0.012 −0.002 0.004

Median 0.015 0.010 −0.001 0.000 0.087 0.005 0.010 −0.001 0.003

Maximum 0.247 0.248 0.177 0.071 6.523 0.139 0.057 0.700 0.026

Minimum −0.306 −0.272 −0.294 −0.069 −5.463 −0.150 −0.005 −0.692 −0.020

SD 0.075 0.075 0.043 0.019 1.273 0.053 0.009 0.145 0.006

Skewness −0.498 −0.369 −1.178 0.349 0.137 −0.437 1.230 0.119 0.099

Kurtosis 4.090 3.553 13.84 6.388 8.264 3.849 5.555 12.55 5.198

Jarque-Bera 18.18 7.100 1,027.2 99.78 231.58 12.41 104.88 761.68 40.62

Probability 0.000 0.028 0.000 0.000 0.000 0.002 0.000 0.000 0.000

Observations 200 200 200 200 200 200 200 200 200

Panel B Correlation matrix

Nifty 1.00

Sensex 0.98 1.00

Bondyield −0.12 −0.14 1.00

EX −0.47 −0.47 0.06 1.00

FII 0.12 0.12 −0.11 −0.21 1.00

IIP 0.08 0.04 0.00 −0.01 −0.02 1.00

M3 −0.00 −0.00 0.00 −0.03 −0.09 0.03 1.00

Treasury −0.01 −0.02 0.00 0.00 −0.08 −0.07 0.03 1.00

Inflation −0.08 −0.08 0.27 0.02 −0.04 −0.10 0.03 −0.05 1.00

Summary statistics show the mean, standard deviation (SD), skewness, kurtosis and Jarque-Bera statisticsfor all monthly macroeconomic variables and Nifty and Sensex indices*, # Significance at 1, 5 % and level, respectively in correlation matrix

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Table 2 Unit root tests statistics

Variables ADF PP KPSS

Interceptwithout trend

Interceptwith trend


Interceptwith trend


Interceptwith trend

Nifty −13.56669* −13.56669* −13.68866* −13.69326* 0.088 0.070

Sensex −13.38663* −13.36517* 13.49226* −13.50459* 0.103 0.073

Bondyield −14.04295* −14.11330* −14.11428* −14.22146* 0.233 0.061

EX −11.86455* −11.83651* −11.82154* −11.82335* 0.124 0.111

FII −21.92833* −21.87833* −29.87319* −29.89983* 0.030 0.020

IIP −25.13193* −25.06793* −26.82507* −26.82524* 0.017 0.018

M3 −14.02705* −13.99933* −14.20629* −14.21805* 0.110 0.115

Treasury −20.89174* −20.92602* −21.17687* −21.33172* 0.143 0.036

Inflation −9.65095* −9.73295* −9.78354* −9.86971* 0.0164 0.026

The table reports the ADF, PP and KPSS tests statistics at level. The optimal lag for ADF test and truncationlag for PP test are selected based on the AIC and SIC criteria. In the case of both ADF and PP tests, thecritical values at 1, 5 and 10 % are −3.46, −2.87 and −2.57, respectively for the model without trend and−4.00, −3.43 and −3.13 for the model with trend. ADF and PP tests examine the null hypothesis of aunit root against the stationary alternative. For fixing the truncation lag for KPSS test, spectral estimationmethods selected is the Bartlett kernel, and for bandwidth it is the Newey–West method. The critical valuesat 1, 5 and 10 % are 0.73, 0.46 and 0.34, respectively for the model without trend and 0.21, 0.14 and 0.11for the model with trend. The KPSS test examines null of stationary* Significant at 1 % level

is negative for two market portfolios and selected macroeconomic variables whichindicate the lower tail of the distribution is thicker than upper tail. This implies thatstock returns and macroeconomic variables decline more often. It further suggests thatmarket portfolio indices are exhibiting non-systematic returns. The kurtosis coefficientvalues for both the market portfolios are positive, which indicates the distribution tobe leptokurtosis or fat tailed distribution. Subsequently, the Jarque-Bera test statis-tics suggest that all variables are non-normal. The correlation matrix suggests thatthere exist insignificant correlations among macroeconomic fundamentals and marketindices (Panel B). To check the stationarity of the series, augmented Dickey-Fuller(ADF) (1981), Phillips Perron (PP) (1988) and Kwaitkowski et al. (KPSS) (1992) unitroot tests statistics reported in Table 2 show that the null of the unit root is rejectedfor both the indices and the selected macroeconomic variables at levels with interceptand trend. In other words, all the selected variables are of I (0) type series.

4.1 GARCH Estimates

The flurry of work to analyze the conditional volatility models are highly motivatedby the existence of stylized facts and salient features of volatility such as, volatilityclustering, volatility asymmetry, leverage effect and time varying characteristics. Toanalyze the volatility characteristics, ARCH effect in the time series is checked bycomputing LM statistics after obtaining the AR (1) model residuals. We use the AR

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Table 3 ARCH-LM teststatistic

* The ARCH effect for allvariables at 1 % level ofsignificance

Variables LM statistic P values

Nifty 4.29* 0.00

Sensex 4.45* 0.00

Bondyield 18.29* 0.00

EX 44.63* 0.00

FII 30.43* 0.00

IIP 28.10* 0.00

M3 6.36* 0.00

Treasury 10.17* 0.00

Inflation 5.68* 0.00

residuals of the monthly stock returns regressed on a constant term and past laggedresiduals values. The selection of lag length along with the log likelihood value is basedon the order of the process that changes in the lag values until it become insignificant.The results presented in Table 3 indicate the presence of ARCH effect in the marketindices and the selected macroeconomic fundamentals. Further, Inclan and Tiao (2002)structural break test is carried out to check the sudden shifts and trend break in thereturns series. Nevertheless, the test results suggest no such breaks in the series touse the dummy variables in the GARCH class of models. We estimate three setsof univariate conditional heteroskedastic models namely GARCH, EGARCH andTGARCH models for two market indices and the selected macroeconomic variables.The estimates of the three models are reported in Panel A, B and C respectivelyin Table 4. The symmetric GARCH model results show that all the coefficients aresignificantly different from zero indicating the volatility persistence in all the variables.Specifically, the coefficients of the ARCH term α and the GARCH term β are founddifferent from zero for two market indices and macroeconomic variables indicating thatthe lagged values of the residuals and the lagged values of the conditional varianceare able to capture the future volatility. Moreover, the values of α and β are closeto 1 indicating a high degree of volatility persistence. The GARCH estimates areinsignificant for FIIs and money supply. The estimates of EGARCH model show thepresence of volatility asymmetry in all variables as the δ < 0 for two market indices andselected macroeconomic variables. This implies negative shock has a greater impacton volatility rather than the positive shocks of the same magnitude.

This kind of phenomenon in the financial time series is reported in several studies(e.g. Campbell and Hentschel 1992; Koutmous and Booth 1995; Chinzara 2011).Notably, it can be asserted that when the entire series exhibit the volatility asymmetrythen the standard GARCH model is not sufficient and appropriate to analyze volatility.Hence, a further comparison will be made between EGARCH and TGARCH models.The TGARCH model is carried out to determine the impact of news effect on stockreturns volatility. The results of the TGARCH model presented in Panel C of Table 4provide the evidence that news impact is asymmetric since γ �= 0. The results arenot consistent with EGARCH model as γ is either γ < 0 and γ > 0 for differentvariables. The values of market index Nifty, IIP, M3, treasury and inflation variablesare found less than zero values. It implies the volatility of these respective variables is

123


Tabl

e4

Est

imat

esof

vola

tility

mod

els

Nif

tySe

nsex

Bon

dyie

ldE

XFI

III

PM

3T

reas

ury

Infla

tion

Pane

lA:

GA

RC

H

ω0.

005

0.00

50.

000

0.00

01.

157

0.00

13.

540.

001

0.00

0

α0.

073*

0.05

20.

550*

0.11

3***

0.27

1**

0.78

3−0

.020

0.16

50.

105

β0.

835*

0.84

9*0.

515*

0.90

0*−0

.041

−0.0

550.

648*

0.79

1*0.

718*

α+β

0.91

80.

901

1.06

51.

013

0.23

00.

728

0.62

80.

956

0.82

3

AR

CH

-LM

4.85

(0.4

3)4.

01(0

.54)

2.55

(0.7

6)0.

48(0

.99)

2.47

(0.7

7)18

.66(

0.00

)1.

17(0

.94)

2.30

(0.8

0)3.

57(0

.61)

AR

CH

-LM

22.

75(0

.73)

7.68

(0.1

7)0.

58(0

.98)

0.40

(0.9

9)0.

15(0

.99)

12.4

5(0.

02)

0.22

(0.9

9)0.

15(0

.99)

8.16

(0.1

4)

LL

234.

2123

4.51

382.

4853

4.30

−316

.74

322.

0964

2.62

126.

5073

3.31

SIC

−2.2

1−2

.21

−3.7

24.

673.

18−3

.28

−6.2

9−1

.26

−7.2

9

Mcl

eod

LiQ

(20)

15.3

322

.33

5.39

1.46

0.49

81.2

548

.32

0.47

19.7

4

Mcl

eod

LiQ

(40)

19.2

631

.14

7.40

6.48

0.94

183.

8549

.95

0.70

23.5

0

Pane

lB:

EG

AR

CH

ω−2

.495

−2.5

04−3

.102

−7.8

70−0

.042

−7.3

48−9

.261

−1.9

48−1

0.09

0

α0.

353*

*0.

376*

*0.

210*

**0.

107*

**0.

0267

1.01

80.

050

0.38

4**

0.05

0

β0.

567*

*0.

569*

*0.

540*

*0.

750*

0.51

9**

−0.0

610.

750*

0.57

9**

0.75

0*

α+β

0.92

00.

945

0.75

00.

857

0.78

60.

957

0.80

00.

963

0.80

0

δ−0

.041

*−0

.111

*−0

.009

*−0

.002

*−0

.306

*−0

.346

*−0

.000

*−0

.095

*−0

.000

*

AR

CH

-LM

5.86

(0.3

1)4.

46(0

.48)

10.2

9(0.

06)

2.09

(0.8

3)0.

85(0

.97)

2.07

(0.7

2)0.

37(0

.99)

0.27

(0.9

9)0.

06(0

.99)

AR

CH

-LM

22.

01(0

.84)

2.92

(0.7

1)1.

32(0

.93)

0.46

(0.9

9)0.

11(0

.99)

7.24

(0.1

2)0.

30(0

.99)

0.17

(0.9

9)0.

04(0

.99)

LL

230.

1423

1.12

353.

3553

3.00

−312

.96

332.

5663

2.22

127.

0073

3.31

SIC

−2.1

3−2

.13

−3.7

6−5

.56

2.97

−3.3

9−6

.32

−1.8

3−7

.29

Mcl

eod

LiQ

(20)

13.4

220

.88

1.89

0.88

0.42

100.

570.

141.

810.

18

Mcl

eod

LiQ

(40)

19.4

533

.75

2.48

2.78

0.64

231.

410.

231.

994.

19

123


Tabl

e4

cont

inue

d

Nif

tySe

nsex

Bon

dyie

ldE

XFI

III

PM

3T

reas

ury

Infla

tion

Pane

lC:

TG

AR

CH

ω0.

004

0.00

060.

0001

4.19

0.51

20.

0002

7.71

0.00

04.

04

α−0

.034

−0.0

190.

342

0.13

3−1

.58

0.25

00.

105

0.20

00.

055

β0.

279

0.84

40.

533

0.81

20.

490.

880

0.20

00.

849

0.83

5

α+β

0.02

450.

825

0.87

50.

945

−1.0

91.

130

0.30

51.

049

0.89

0

γ−0

.008

0.09

80.

399

0.26

10.

201

−0.4

73−0

.371

−0.0

82−0

.004

AR

CH

-LM

4.19

(0.5

2)4.

59(0

.46)

3.72

(0.5

8)3.

04(0

.69)

0.82

(0.9

7)6.

12(0

.29)

1.25

(0.9

3)0.

75(0

.97)

3.21

(0.6

6)

AR

CH

-LM

21.

76(0

.88)

6.13

(0.2

9)2.

84(0

.72)

1.20

(0.9

4)0.

06(0

.99)

3.19

(0.6

7)0.

27(0

.99)

0.26

(0.9

9)13

.59(

0.01

)

LL

232.

2523

4.04

386.

1354

3.79

−302

.92

348.

3364

5.37

139.

7173

9.39

SIC

−2.1

7−2

.19

−3.7

2−5

.30

3.20

−3.3

4−6

.32

−1.2

4−7

.27

Mcl

eod

LiQ

(20)

19.2

924

.81

8.99

2.29

0.41

49.3

035

.68

1.19

21.7

4

Mcl

eod

LiQ

(40)

21.2

337

.67

12.5

86.

640.

6914

4.22

37.0

51.

6027

.40

Inth

ista

ble,

theω

deno

tes

the

cons

tant

valu

es,α

deno

tes

the

AR

CH

term

,βth

eG

AR

CH

term

andα

+β

pres

ents

the

stat

iona

ryco

nditi

onof

mod

elan

dvo

latil

itype

rsis

tenc

eC

oeffi

cien

tval

ues

ofth

eG

AR

CH

,EG

AR

CH

and

TG

AR

CH

mod

els

have

been

repo

rted

fort

hean

alys

is.T

hefig

ures

inth

epa

rent

hesi

sin

AR

CH

-LM

test

ssh

owth

ere

spec

tive

pva

lues

*,**

and

***

deno

tes

the

sign

ifica

nce

leve

lat1

,5an

d10

%,r

espe

ctiv

ely

123


much more influenced by the bad news, whereas, the volatility of Sensex, bond yield,exchange rate and FIIs are significantly influenced by the good news. This indicatesthat good news and bad news both simultaneously does increase the volatility in themarket suggesting the existence of leverage effect. The reported results are consistentwith the findings of Chinzara and Aziakpono (2009) and Chinzara (2011). All thethree selected models appear to be capturing the persistence of volatility appropriately,as it can be seen from the insignificant ARCH-LM and ARCH-LM2statistic valuesalong with p-values for three set of models. Further, for the robustness check in theresiduals and the squared residuals drawn from the respective conditional models,McLeod and Li (1983) test is carried out in the present study. The results revealthat there is no autocorrelation in the residuals and squared residuals drawn fromthe three sets of GARCH models. Further, the selection of best model among thethree models is based on the log likelihood value and SIC criteria. According tothe criteria, the lower the SIC, the better the model. In the present study, the SICsuggest EGARCH model is superior model over the other models. Hence, the furtheranalysis is carried out with the EGARCH model. The conditional volatility for eachmacroeconomic fundamentals and stock market indices have been extracted fromEGARCH and conditional volatility series are further analyzed using the multivariateVAR framework. Table 5 shows the descriptive statistics for the conditional monthlyvolatility of stock returns and macroeconomic variables extracted from the EGARCHmodel. The statistics for conditional volatility series reveal several salient features. Thevolatility of stock market indices and macroeconomic fundamentals show the positiveskewness which indicates upper tail of the distribution is thicker than lower tail. Thekurtosis coefficient values for both market portfolios and macroeconomic variablesare positive, indicating the distribution to be leptokurtic or fat tailed distribution. Thevolatility of the Nifty is lower than the volatility of Sensex. Further, the Jarque-Beratest statistic suggests that chosen data series are non-normally distributed.

4.2 Multivariate VAR Results

Multivariate VAR is implemented on extracted conditional volatility measured fromEGARCH model to analyze the relationship between stock market volatility andmacroeconomic volatility. The estimates of VAR model are documented in Table 6.The results are varying across the different proxies used for stock market returns.For BSE Sensex, we found money supply and inflation statistically significant. Thefindings imply that the volatility of money supply and inflation affect the stock returnvolatility. In case of Nifty index, the ten years bond yield and inflation are found statis-tically significant. However, results for the other major macroeconomic fundamentalsnamely long term bond yield, EX rate, FIIs, IIP and Treasury bill rate are not significantto influence the stock market volatility. It reflects that volatility in these macroeco-nomic fundamentals plays minimal role as systematic economic news to influence thestock market volatility. Further, it is quite interesting to investigate whether the stockmarket volatility has any impact on volatility of macroeconomic variables by using themultivariate VAR framework. The estimates are presented in Table 6 where we findBSE Sensex is statistically significant in explaining the volatility of macroeconomic

123


Tabl

e5

Des

crip

tive

stat

istic

sof

cond

ition

alvo

latil

ity

Sum

mar

yst

atis

tics

for

the

cond

ition

alva

rian

cese

ries

EG

AR

CH(h

t)

Nif

tySe

nsex

Bon

dyie

ldE

XFI

III

PM

3T

reas

ury

Infla

tion

Mea

n0.

005

0.00

50.

002

0.00

0489

1.03

11.

93E−0

39.

19E−0

50.

0252

053.

47E−0

5

SD0.

001

0.00

050.

0043

0.00

0507

0.04

16.

86E−0

43.

45E−0

50.

0586

331.

35E−0

6

Skew

ness

11.5

51.

1013

7.82

91.

4121

89−1

.466

1.85

4655

−0.3

4661

96.

4026

63−2

.212

8

Kur

tosi

s15

1.69

5.60

479

.34

4.78

9498

8.43

69.

1286

841.

8655

8250

.026

4532

.921

65

Jarq

ue-B

era

187,

747.

396

.461

3950

,363

.692

.696

0231

6.44

8642

5.52

6314

.655

3819

,696

.55

7,58

5.97

2

Obs

erva

tions

199

199

199

199

199

199

199

199

199

Sum

mar

yst

atis

tics

show

the

mea

n,st

anda

rdde

viat

ion,

skew

ness

,kur

tosi

san

dJa

rque

-Ber

ast

atis

tics

for

cond

ition

alvo

latil

ityof

all

mon

thly

mac

roec

onom

icva

riab

les

and

Nif

tyan

dSe

nsex

indi

ces

123


Table 6 Multivariate VAR F-statistics

Bond yield EX FII IIP M3 Treasury Inflation

(A) Predictive power of stock market volatility

1. Sensex 1.24 1.21 1.10 0.80 4.36∗ 1.27 2.13∗(0.29) (0.30) (0.35) (0.52) (0.00) (0.28) (0.078)

2. Nifty 4.24∗ 1.25 0.96 0.36 1.18 1.58 2.28∗(0.00) (0.29) (0.42) (0.83) (0.32) (0.18) (0.06)

(B) Predictive power of macroeconomic volatility

1. Sensex 9.03∗ 0.43 2.95∗ 0.63 1.44 0.22 1.57∗(0.00) (0.78) (0.02) (0.63) (0.22) (0.92) (0.09)

2. Nifty 1.00 0.79 1.15 1.08 0.47 0.54 1.47

(0.40) (0.53) (0.33) (0.36) (0.75) (0.70) (0.21)

The figures in the parenthesis show the respective p values. The critical values for the F-statistic are 1.57and 1.42 at 5 and 10 %, respectively*, ** 5 and 10 % significance level, respectively

variables such as long-term bond yield, FIIs and inflation. It implies that stock marketvolatility directly transmit to macroeconomic volatility. This implies that investorswould like to keep an eye on these fundamentals for investment strategy. The find-ings suggest bidirectional causality between the stock market returns and inflation andunidirectional causality between stock market returns and long term bond yield, FIIsand money supply which is consistent with the findings of Errunza and Hogan (1998),Abugri (2008) and Chinzara (2011). The multivariate VAR framework of economicrelations is the reduced form of structure to remove the simultaneity in the model.Therefore, often VAR approach treats all lagged values of endogenous variables inthe system together. This approach induces collinearity among the innovations of vec-tors and the errors and reduces the individual role of macroeconomic fundamentals.To capture the individual role of macroeconomic fundamentals, we use the impulseresponse function analysis, block exogeneity and variance decomposition test (Fig. 1).

To examine the continuous persistence, speed and sign of volatility and responseof stock market volatility to the unit shock in the macroeconomic variables and viceversa, 10 months impulse response functions are estimated by employing the Choleskyresponse method. A shock to the i th variable not only directly affects the i th variablebut is also transmitted to all of the other endogenous variables through the dynamic(lag) structure of the VAR. An impulse response function traces the effect of a one-time shock to one of the innovations on current and future values of the endogenousvariables. Figure 2 plots the graphs of impulse response functions. The thick lines inthe middle represent the estimates of impulse responses whereas dotted lines aroundimpulse response represent standard error bands. If standard error bands tappers tozero, the effect is significant. In the plotted figures, the response of stock marketvolatility to macroeconomic volatility and macroeconomic volatility to stock marketvolatility is analyzed simultaneously.

The responses of stock market volatility to unit shocks in macroeconomic volatilityare diverse. Figure 2 shows that Sensex is highly sensitive to the response of long term

123


Conditional Volatility of Sensex

Period of Time

Co

nd

itio

nal

Vo

lati

lity

25 50 75 100 125 150 1750.003

0.004

0.005

0.006

0.007

0.008

0.009

Conditional Volatility of Nifty

Period of Time

Co

nd

itio

nal

Vo

lati

lity

25 50 75 100 125 150 1750.0025

0.0050

0.0075

0.0100

0.0125

0.0150

0.0175

0.0200

0.0225

0.0250

Conditional Volatility of Bondyield

Period of Time

Co

nd

itio

nal

Vo

lati

lity

20 40 60 80 100 120 140 160 180

0.00

0.01

0.02

0.03

0.04

0.05

0.06

ConditionalVolatility of Exchange Rate

Period of Time

Co

nd

itio

nal

Vo

lati

lity

25 50 75 100 125 150 1750.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

Conditional Volatility of Foreign Institutional Investment

Period of Time

Co

nd

itio

nal

Vo

lati

lity

20 40 60 80 100 120 140 160 1800.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

Conditional Volatility of Index of Industrial Production

Period of Time

Co

nd

itio

nal

Vo

lati

lity

25 50 75 100 125 150 175

0.001

0.002

0.003

0.004

0.005

0.006

Conditional Volatility of Broad Money Supply M3

Period of Time

Co

nd

itio

nal

Vo

lati

lity

25 50 75 100 125 150 1750.000000

0.000025

0.000050

0.000075

0.000100

0.000125

0.000150

Conditional Volatility of Treasury Bill Rate

Period of Time

Co

nd

itio

nal

Vo

lati

lity

20 40 60 80 100 120 140 160 1800.0

0.1

0.2

0.3

0.4

0.5

0.6

Conditional Volatility of Wholesale Price Index

Period of Time

Co

nd

itio

nal

Vo

lati

lity

25 50 75 100 125 150 175

0.0000225

0.0000250

0.0000275

0.0000300

0.0000325

0.0000350

0.0000375

0.0000400

Fig. 1 Plots of conditional volatility stock market indices and macroeconomic variables estimated fromEGARCH model

interest rate. It suggests a better investment strategy based on the prevailing long terminterest rates. Similarly, the response of FIIs is persistent in the Indian equity marketvolatility because of dominance of FIIs in the Indian stock market. In other words,the FIIs create massive volatility in Indian stock market. The response of shocksto money supply is negative and significant. This finding suggests the increase inmoney supply can lead to higher inflation and lower returns. Further, the innovationsin EX, IIP, Treasury bill rate are insignificant. The shocks to the macroeconomicvolatility influence the idiosyncratic and systematic risk, and consequently affect thestock market volatility. Therefore, rational investors always rebalance their portfoliosaccording to the changes in the macroeconomic fundamentals. These findings areconsistent with the findings of Abugri (2008), Corradi et al. (2006) and Chinzara(2011).

The results regarding the response of macroeconomic volatility in response to thechange in stock market volatility are insignificant in case of long term interest rate andexchange rate, whereas, the FIIs volatility in response to the stock market volatility isnegative in the first four months but turned positive thereafter. It explicitly indicatesthat FIIs decisions of rebalancing portfolios by FIIs always based on the volatilenature of markets to earn risk adjusted returns. The variables namely, IIP, inflation,interest rates and money supply do not respond to the shocks in the stock marketvolatility. Furthermore, the volatility of these macroeconomic fundamentals invariantto the shocks in the stock market volatility.

The block exogeneity test analyzes the individual role of macroeconomic variablesin explaining the stock market volatility via making the lags of other variables zero.The test statistics in Table 7 reveal the statistical significance of all macroeconomicvariables and thus explains the individual role of macroeconomic variables to explain

123


-.0002.0000.0002.0004.0006.0008

1 2 3 4 5 6 7 8 9 10

Response of SENSEX to TREASURY

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

Response of TREASURY to SENSEX

-.0002

.0000

.0002

.0004

.0006

.0008

1 2 3 4 5 6 7 8 9 10

Response of SENSEX to M3

-.00001

.00000

.00001

.00002

.00003

.00004

1 2 3 4 5 6 7 8 9 10

Response of M3 to SENSEX

-.0002

.0000

.0002

.0004

.0006

.0008

1 2 3 4 5 6 7 8 9 10

Response of SENSEX to INFLATION

-.0000004

.0000000

.0000004

.0000008

.0000012

1 2 3 4 5 6 7 8 9 10

Response of INFLATION to SENSEX

-.0002.0000.0002.0004.0006.0008

1 2 3 4 5 6 7 8 9 10

Response of SENSEX to IIP

-.0002-.0001.0000.0001.0002.0003.0004.0005

1 2 3 4 5 6 7 8 9 10

Response of IIP to SENSEX

-.0002.0000.0002.0004.0006.0008

1 2 3 4 5 6 7 8 9 10

Response of SENSEX to FII

-.02-.01.00.01.02.03.04.05

1 2 3 4 5 6 7 8 9 10

Response of FII to SENSEX

-.0002.0000.0002.0004.0006.0008

1 2 3 4 5 6 7 8 9 10

Response of SENSEX to EXCHANGERATE

-.0001

.0000

.0001

.0002

1 2 3 4 5 6 7 8 9 10

Response of EXCHANGERATE to SENSEX

-.0002

.0000

.0002

.0004

.0006

.0008

1 2 3 4 5 6 7 8 9 10

Response of SENSEX to BONDYIELD

-.001

.000

.001

.002

.003

.004

1 2 3 4 5 6 7 8 9 10

Response of BONDYIELD to SENSEX

Response to Cholesky One S.D.Innovations ± 2S.E.

-.0001.0000.0001.0002.0003.0004

1 2 3 4 5 6 7 8 9 10

Response of NIFTY to TREASURY

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

Response of TREASURY to NIFTY

-.0001.0000.0001.0002.0003.0004

1 2 3 4 5 6 7 8 9 10

Response of NIFTY to M3

-.00001.00000.00001.00002.00003.00004

1 2 3 4 5 6 7 8 9 10

Response of M3 to NIFTY

-.0001

.0000

.0001

.0002

.0003

.0004

1 2 3 4 5 6 7 8 9 10

Response of NIFTY to INFLATION

-.0000004

.0000000

.0000004

.0000008

.0000012

1 2 3 4 5 6 7 8 9 10

Response of INFLATION to NIFTY

-.0001.0000.0001.0002.0003.0004

1 2 3 4 5 6 7 8 9 10

Response of NIFTY to IIP

-.0002-.0001.0000.0001.0002.0003.0004.0005

1 2 3 4 5 6 7 8 9 10

Response of IIP to NIFTY

-.0001.0000.0001.0002.0003.0004

1 2 3 4 5 6 7 8 9 10

Response of NIFTY to FII

-.02-.01.00.01.02.03.04.05

1 2 3 4 5 6 7 8 9 10

Response of FII to NIFTY

-.0001.0000.0001.0002.0003.0004

1 2 3 4 5 6 7 8 9 10

Response of NIFTY to EXCHANGERATE

-.0001

.0000

.0001

.0002

1 2 3 4 5 6 7 8 9 10

Response of EXCHANGERATE to NIFTY

-.0002-.0001.0000.0001.0002.0003.0004

1 2 3 4 5 6 7 8 9 10

Response of NIFTY to BONDYIELD

-.001.000.001.002.003.004

1 2 3 4 5 6 7 8 9 10

Response of BONDYIELD to NIFTY

Fig. 2 Impulse response function graphs. To save the space all the impulse response function graphsestimated from the VAR models not reported. The results are available on request

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Table 7 Block exogeneity test statistics

Bond yield EX FII IIP M3 Treasury Inflation

(A) Predictive power of stock market volatility

1. Sensex 5.55* 6.16* 3.01* 6.99* 18.63* 6.32* 6.77*

2. Nifty 17.72* 6.42* 2.33* 2.39* 3.48* 7.38* 3.81*

(B) Predictive power of macroeconomic volatility

1. Sensex 39.41* 0.59 16.99* 2.67* 8.66* 1.57 2.85*

2. Nifty 7.38* 2.70* 2.60* 3.31* 4.34* 2.36* 1.42

* The significance at 1 % level

the volatility in stock market. The stock market volatility influences macroeconomicvolatility of each macroeconomic variable with the exception of exchange rate andTBR volatility. Furthermore, the Nifty index is significant to influence volatility in allthe selected macroeconomic variables except inflation. These findings are in contrastto the findings of Schwert (1989), Morelli (2002), Chinzara (2011). Largely, it can beinferred that there exist a bidirectional causal linkage between the volatility of stockmarket returns and the macroeconomic variables in India.

After identifying the bidirectional causal relationship between macroeconomicvolatility and equity market volatility, it is important to understand, what proportionof stock market volatility is explained by the volatility of each individual economicfundamental and what proportion of macroeconomic volatility is explained by theindividual stock return indices. To analyze this, we estimated the 12-month variancedecomposition functions using the conditional variance extracted from EGARCH.

The variance decomposition explains how much percentage of equity marketvolatility is explained by each individual macroeconomic fundamental and vice versa.It is explicit from the Tables 8 and 9 that the variation of equity market volatility isbecause of macroeconomic fundamentals. Besides, the first, sixth and twelve monthvariance decomposition are estimated to understand the magnitude of different vari-ables during different periods. Volatility in long term bond yield, exchange rate, FIIs,inflation and money supply seem to significantly affect the stock market, except IIP.More specifically, it is evident that macroeconomic variables explain more than 80percent variation in the Sensex and Nifty indices. The explanation for this nature can bethat the stock market volatility is sensitive to the variance of macroeconomic variables.In detail, because of the fluctuations in interest rate, there will be changes in the costof capital which leads to change in the future cost of investments payments. Second,the volatility of exchange rate creates huge gap in CAD through imbalance in exportsand imports, which further induces shift in future cash flows and future earnings of thefirms leading to volatile returns of the companies. Further, if general price level varies,it creates change in the nominal and real returns and finally the volatility of the returns.The volatility of money supply induces shift in the future strength of the volatility ofexpected future cash flows in the economy. Hence, the individual variation of thesefundamentals have greater role to influence the volatility of the stock market.

In other words, the findings indicate that the macroeconomic variables play signifi-cant role in forecasting the future volatility of stock market. Further, if we focus on the

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Table 8 Variance decomposition functions—sensex

Period SE Sensex Bondyield EX FII IIP M1 M3 Treasury Inflation

Variance decomposition of Sensex

1 0.0005 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6 0.0006 77.45 0.80 0.94 1.59 1.80 2.39 9.96 2.58 2.46

12 6.11E−04 76.44 0.89 1.08 1.76 1.84 2.44 10.02 2.67 2.81

Variance decomposition of Bondyield

1 0.003 1.132 98.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6 0.004 20.29 67.63 2.35 1.37 1.73 0.35 3.52 0.12 2.60

12 0.004 20.70 62.61 2.28 1.44 3.02 1.11 4.04 1.70 3.06

Variance decomposition of EX

1 0.0002 1.68 1.33 96.97 0.00 0.00 0.00 0.00 0.00 0.00

6 0.0003 0.76 2.13 92.08 1.67 1.20 0.58 0.96 0.50 0.08

12 0.0004 0.82 2.43 92.46 1.74 0.77 0.43 0.86 0.37 0.07

Variance decomposition of FII

1 0.03 0.93 0.50 3.20 95.35 0.00 0.00 0.00 0.00 0.00

6 0.04 7.61 3.63 4.37 77.12 0.87 1.58 2.34 0.70 1.73

12 0.04 7.51 3.71 4.59 75.92 1.07 1.62 2.61 1.01 1.92

Variance decomposition of IIP

1 0.0004 0.18 0.15 0.10 0.34 99.20 0.00 0.00 0.00 0.00

6 0.0006 1.082 1.40 4.02 1.34 78.41 9.92 1.21 1.64 0.94

12 0.0006 1.22 1.44 6.48 1.88 73.29 11.09 1.66 1.97 0.92

Variance decomposition of M3

1 3.44E−05 0.34 0.45 1.98 0.19 0.56 0.47 95.99 0.00 0.00

6 3.79E−05 2.87 1.83 2.83 0.99 3.45 1.28 80.96 2.94 2.81

12 3.83E−05 3.35 1.82 3.25 1.08 3.63 1.53 79.43 3.02 2.84

Variance decomposition of treasury

1 0.05 3.27 0.19 0.11 0.96 0.05 0.25 0.83 94.30 0.00

6 0.06 3.24 0.95 0.57 2.08 2.96 1.40 3.38 81.27 4.12

12 0.06 3.40 1.18 0.57 2.09 3.09 1.54 3.42 80.35 4.32

Variance decomposition of inflation

1 1.06E−06 3.13 0.05 0.02 1.45 1.71 0.26 0.72 0.79 91.82

6 1.16E−06 5.14 0.96 1.45 3.37 3.11 2.29 1.97 1.16 80.51

12 1.18E−06 5.07 1.16 2.30 3.42 3.43 2.41 1.92 1.53 78.71

extent to which the volatility of macroeconomic variables generated through the vari-ability in the stock market volatility, the results are mixed. Interestingly, the volatilityof bond yield, exchange rate, FIIs, broad money supply and inflation are influenced bythe volatility of stock market except the TBR and IIP. Within the first month, the pre-vious lagged values explain the volatility of macroeconomic variables. Nevertheless,in the sixth and tenth period, the volatility of the stock market significantly influencesthe variance of macroeconomic variables. This is an indication that the stock market

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Table 9 Variance decomposition function—Nifty

Period SE Nifty Bondyield EX FII IIP M1 M3 Treasury Inflation

Variance decomposition of Nifty

1 0.0003 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6 0.0004 77.71 6.60 2.60 1.62 1.37 1.48 3.02 5.02 0.53

12 0.0004 72.60 10.51 2.58 1.51 1.62 1.72 3.41 5.06 0.95

Variance decomposition of Bondyield

1 0.003 0.576 99.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6 0.004 1.007 91.45 1.20 0.38 1.89 0.30 2.92 0.35 0.45

12 0.0048 0.99 87.68 1.361 0.48 3.03 0.85 2.94 2.08 0.55

Variance decomposition of EX

1 0.0001 8.71 0.61 90.66 0.00 0.00 0.00 0.00 0.00 0.00

6 0.0003 5.17 1.95 87.68 1.68 1.61 0.67 0.80 0.32 0.08

12 0.0004 3.94 3.15 87.90 1.92 1.03 1.04 0.68 0.23 0.06

Variance decomposition of FII

1 0.04 4.84 0.15 1.46 93.53 0.00 0.00 0.00 0.00 0.00

6 0.04 4.99 2.54 2.94 82.04 0.86 1.57 2.37 0.75 1.90

12 0.04 5.0 2.69 3.15 81.02 1.01 1.60 2.58 0.94 1.95

Variance decomposition of IIP

1 0.0004 0.09 0.01 0.13 0.19 99.55 0.00 0.00 0.00 0.00

6 0.0006 0.87 0.96 4.97 1.74 77.60 10.18 1.01 1.28 1.34

12 6.34E−04 0.82 0.95 7.73 2.40 72.47 11.59 1.12 1.61 1.26

Variance decomposition of M3

1 3.49E−05 0.084 1.58 2.93 0.06 0.89 0.49 93.93 0.00 0.00

6 3.80E−05 0.65 3.03 3.45 0.65 3.41 1.35 81.03 2.61 3.78

12 3.83E−05 0.77 3.23 4.00 0.68 3.53 1.50 79.87 2.63 3.76

Variance decomposition of treasury

1 5.12E−02 7.07 0.28 0.83 0.08 0.02 0.40 0.80 90.49 0.00

6 6.51E−02 5.97 1.27 1.18 0.69 2.59 1.35 3.35 78.62 4.94

12 0.065512 5.99 1.59 1.19 0.79 2.73 1.68 3.42 77.55 5.02

Variance decomposition of inflation

1 1.07E−06 0.35 0.0003 0.07 2.01 1.88 0.28 0.71 0.33 94.32

6 1.16E−06 1.96 1.67 1.31 3.69 3.22 2.49 1.74 1.00 82.85

12 1.18E−06 1.94 1.98 2.01 3.71 3.57 2.60 1.75 1.47 80.93

volatility has impact on variation in macroeconomic fundamentals in longer time hori-zon. The inference from the results is that the conditional macroeconomic and stockmarket volatility affect each other partially. Although the present findings are first oftheir kind in Indian context, but such results were widely documented in developedmarkets (e.g. Schwert 1989; Whitelaw 1994; Beltratti and Morana 2006).

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5 Conclusion

The study empirically examined the theoretical linkages between equity market volatil-ity and macroeconomic volatility in emerging Indian equity market. Unlike previousstudies, the present study has carried out two stage empirical estimation. First, the timevarying conditional symmetric and asymmetric univariate GARCH class of modelswere employed to extract the conditional volatility. Then the conditional varianceseries extracted from the first step estimation is used for the multivariate VAR modelalong with impulse response function, block exogeneity and variance decompositionfunction to find out the possible relationship and direction of causality between equitymarket volatility and macroeconomic volatility. Besides, to identify the break in theconditional variance, the present study has employed the structural break test.

The persistence of volatility and asymmetric effect is evident from estimates ofthe GARCH, EGARCH and TGARCH models. The results from multivariate VARindicates a significant relationship between equity market volatility and volatility ofselected macroeconomic variables namely the long term bond yield, broad moneysupply and inflation. In other words, the empirical results validate the ability of somemacroeconomic factors volatility to explain the stock market volatility. Further, itis observed that there is bidirectional causality between stock market volatility andinflation and a unidirectional relationship between market proxy BSE Sensex andlong term bond yield, FIIs and money supply. This relation shows the increasinginterdependence of financial markets and macroeconomic fundamentals in India.

The findings of present study have certain theoretical and practical implications.The investors interested in Indian stock market can improve the forecasting by incor-porating macroeconomic variables like long term interest rates FIIs inflation and fora better investment decisions. The predictable power of macroeconomic volatilityinvalidates semi-strong form of informational efficiency in Indian context. In back-drop of the results, it is advisable to consider equity market volatility in formulatingmacroeconomic stability policies.

Acknowledgments We thank two anonymous referees and Editor of the journal for their constructivecomments and suggestions. Usual disclaimer applies.

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