This paper explores to develop alternative models from the Autoregressive Conditional Heteroscedasticity (ARCH) or its generalization, the Generalized ARCH(GARCH) family, to estimate volatility in the Indian equity market return. The different volatility estimators and model have been proposed in the literature to measure volatility returns. The ARCH effect or such influence is evidently persistent for long time periods. We have tried to capture this effect through different GARCH type models because high variability and high volatility has been seen in stock exchange rates, daily, weekly and monthly stock market returns, foreign exchange rates. The main idea underlying this study is to identify and estimate the mean and variance components of the daily closing share prices of the ARIMA- GARCH type models by explaining the volatility structure of the residuals obtained under the best suited mean fort he said series. The study concludes that there is a presence of volatility clustering, evidence of asymmetric effect on volatility of risk premium in the stock market.

Keywords: ARIMA (Autoregressive Integrated Moving Average), EARCH (Exponential Autoregressive Conditional (Heteroscedasticity), TARCH (Threshold Autoregressive Conditional Heteroscedasticity).