Traditional econometric models assume a constant one period forecast variance. However, many financial time series display volatility clustering, that is, autoregressive conditional heteroscedasticity (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-of sample forecast accuracy. The paper also investigates whether there is any change in volatility after the introduction of futures. The estimation of volatility is made at the macro level on a major market index, namely, S&P CNX Nifty. In addition, 50 individual companies' share prices currently included in S&P CNX Nifty are used to examine the heteroscedastic behaviour of the Indian stock market at the micro level. The volatility is estimated by fitting different models to the market indices by dividing the study duration into two different time period's, one pre-future and another post-futures -:
• Historical moving average model
• Standard Generalized autoregressive conditional heterosedasticity GARCH (1, 1) model.
The paper found:
• 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.