Academic journal article IUP Journal of Applied Economics

Modeling Volatility for the Indian Stock Market

Academic journal article IUP Journal of Applied Economics

Modeling Volatility for the Indian Stock Market

Article excerpt

This paper is an attempt to model the volatility of the equity data of the two Indian stock markets. The study found volatility clustering in the daily returns of indices. Different GARCH models were estimated for various indices of NSE and BSE, the two premier Indian stock exchanges. GARCH(1, 1) with MA(1) in the mean equation was found to fit better than the other models. The models were used to test the spillover effect between the benchmark indices of the two Indian markets, to test for the possibility of volatility transmission within a country and between the two exchanges. The study found volatility transmission between the two markets.

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Modeling and forecasting stock market volatility has been the subject of interest around the world. Vast empirical and theoretical investigations have been undertaken by academicians as well as practitioners in finance. In finance literature different approaches have been used to measure volatility. One of the simplest models of volatility is the historical estimate, which involves calculating standard deviation or variance of asset returns. It is used as the elementary measure of risk of financial assets. The second class of model is implied volatility models. Implied volatility is the market's forecast of the volatility of the underlying asset returns over the lifetime of the option. The third class of model is ARCH/GARCH class of models, which are most extensively used in the finance literature. The justification for the use of this type of models comes from certain features of financial asset returns viz., volatility clustering and time-varying conditional variance. The classical linear regression model assumes that the variance of the errors remains constant, but this is unlikely in the context of financial time series. So it becomes necessary to overcome these limitations, and the volatility models like ARCH/GARCH models are better suited for estimating volatility of the financial time series than the historical volatility models.

Several studies have investigated volatility patterns in developed and emerging markets from various dimensions. Difference in the volatility pattern among developed and emerging markets, time-varying relationship between volatility and other variables, impact of structural changes on volatility, and international transmission of volatility are some of vastly investigated areas. The transmission of information across international equity markets has been the subject of interest over the past decade or so, mainly due to the increased integration of markets around the world. The studies have investigated the dynamic relations of returns and volatility across markets. In the theoretical literature, we find two broad categories of explanations for comovement in stock market returns. First, the aggregate shocks in one country could affect the fundamentals of stock prices in more than one country. These are the shocks which are basically related to the information regarding the fundamentals, which affects the stock prices. Second explanation is contagion, which is not explained by fundamentals.

Review of Literature

Forbes and Rigobon (1999) examined the phenomenon of stock market comovements and the conceptual issues involved in testing for contagion. They argued that while testing for contagion across markets, it is extremely important to measure the comovement accurately; the correlation coefficient is conditional on market volatility. So an unadjusted cross-market correlation coefficient is biased upwards during the period of turmoil, when market volatility increases. This study has found that after applying adjusted correlation coefficients to the standard tests for contagion, less evidence of contagion was found during the East Asian Financial Crisis, Mexican Crisis, and the US Stock Market Crash of 1987.

King and Wadhwani (1989) examined the transmission of volatility between stock markets. …

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