Traditional econometric models assume a constant one period forecast variance. However, many financial time series display volatility clustering, that is, autoregressive conditional heteroskedasticity (ARCH). The aim of this paper is to estimate conditional volatility models in an effort to capture the salient features of stock market volatility in India and evaluate the models in terms of out-ofsample forecast accuracy. The paper also investigates whether there is any leverage effect in Indian companies. The estimation of volatility is made at the macro level on two major market indices, namely, S&P CNX Nifty and BSE Sensex. The fitted model is then evaluated in terms of its forecasting accuracy on these two indices. In addition, 50 individual companies' share prices currently included in S&P CNX Nifty are used to examine the heteroskedastic behaviour of the Indian stock market at the micro level. The vanilla GARCH (1, 1) model has been fitted to both the market indices. We find: a strong evidence of time-varying volatility a tendency of the periods of high and low volatility to cluster a high persistence and predictability of volatility. Conditional volatility of market return series from January 1991 to June 2003 shows a clear evidence of volatility shifting over the period where violent changes in share prices cluster around the boom of 1992. Though the higher price movement started in response to strong economic fundamentals, the real cause for abrupt movement appears to be the imperfection of the market. The forecasting ability of the fitted GARCH (1, 1) model has been evaluated by estimating parameters initially over trading days of the in-sample period and then using the estimated parameters to later data, thus forming out-of-sample forecasts on two market indices. These out-of-sample volatility forecasts have been compared to true realized volatility. Three alternative methods have been followed to measure three pairs of forecast and realized volatility. In each method, the volatility forecasts are evaluated and compared through popular measures. To examine the information content of forecasts, a regression-based efficiency test has also been performed. It is observed that the GARCH (1, 1) model provides reasonably good forecasts of market volatility. While turning to 50 individual underlying shares, it is observed that the GARCH (1, 1) model has been fitted for almost all companies. Only for four companies, GARCH models of higher order may be more successful. In general, volatility seems to be of a persistent nature. Only eight out of 50 shares show significant leverage effects and really need an asymmetric GARCH model such as EGARCH to capture their volatility clustering which is left for future research. The implications of the study are as follows: The various GARCH models provide good forecasts of volatility and are useful for portfolio allocation, performance measurement, option valuation, etc. Given the anticipated high growth of the economy and increasing interest of foreign investors towards the country, it is important to understand the pattern of stock market volatility in India which is time-varying, persistent, and predictable. This may help diversify international portfolios and formulate hedging strategies.

This article investigates the heteroscedastic behaviour of the Indian stock market using different GARCH models. First, the standard GARCH approach is used to investigate whether stock return volatility changes over time and if so, whether it is predictable. Then, the EGARCH models are applied to investigate whether there is asymmetric volatility. Finally, (E) GARCH in the mean extension has been tried to examine the relation between market risk and expected return. The investigation has been made on market index S&P CNX Nifty for a period of 14 and a half years from July 1990 to December 2004. The study reports an evidence of time varying volatility which exhibits clustering, high persistence and predictability. It is found that the volatility is an asymmetric function of past innovation, rising proportionately more during market decline. It is also evidenced that return is not significantly related to risk. The findings are useful to policy makers and to all market participants for pricing derivatives and designing dynamic trading strategies.

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In a perfectly functioning world, every piece of information should be reflected simultaneously in the underlying spot market and its futures markets. However, in reality, information can be disseminated in one market first and then transmitted to other markets due to market imperfections. And, if one market reacts faster to information than the other, a lead-lag relation is observed The lead-lag relationship in returns and volatilities between spot and futures markets is of interest to academics, practitioners, and regulators. In India, there are very few studies which have investigated the lead-lag relationship in the first moment of the spot and futures markets This study investigates the lead-lag relationship in the first moment as well as the second moment between the S&P CNX Nifty and the Nifty future. It also investigates how much of the volatility in one market can be explained by volatility innovations in the other market and how fast these movements transfer between these markets. It conducts Multivariate Cointegration tests on the long-run relation between these two markets. It investigates the daily price discovery process by exploring the common stochastic trend between the S&P CNX Nifty and the Nifty future based on vector error correction model (VECM). It examines the volatility spillover mechanism with a bivariate BEKK model. Finally, this study captures the effects of recent policy changes in the Indian stock market. The results reveal the following: The VECM results show that the Nifty futures dominate the cash market in price discovery. The bivariate BEKK model shows that although the persistent volatility spills over from one market to another market bi-directionally, past innovations originating in future market have the unidirectional significant effect on the present volatility of the spot market. The findings of the study thus suggest that the Nifty future is more informationally efficient than the underlying spot market. These findings may provide insights on the information transaction and index arbitrage between the CNX Nifty and futures markets.

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The study investigates price discovery in the Indian stock market and finds that spot market plays a dominating role in price discovery when it is estimated for the entire period as a whole. However, periodic measures of price discovery suggest that it does not remain the same throughout the period, but varies with time. Panel data analysis also indicates that spot market is more efficient in price discovery for majority of size and sector panels. Finally, while market state‐related variables are found to impact information shares in a majority of the cases, macroeconomic announcements rarely predict the price discovery.