Modeling Time-Varying Volatility in Indian Commodity Futures Return : Some Empirical Evidence

The aim of this paper is to introduce several volatility models and use these models to predict the conditional variance of the rate of return in Indian commodity future market. This paper chooses the Generalized Autoregressive Conditional Heteroscedasticity (GARCH), E-GARCH, GJRGARCH and APARCH models to analyze the rate of return and considers using three different distributions on error terms: normal distribution, Student's t distribution and skewed t distribution. So this paper mainly captures the forecasting performance with volatility models under different error distributions. Finally, by using AIC, the best model is chosen to predict the conditional variance. Forecasting performance is checked by using Mean Square Error (MSE), Heteroskedasticity Adjusted Squared Error (HASE), Logarithmic Error (LE) and Mincer Zarnowitz Regression. This paper selects three Generic 1st Future Contracts traded on MCX (Multi-Commodity Exchange of India): Aluminum, Copper and Zinc. It is concluded that long memory is an important characteristic of the Aluminum, Copper and Zinc futures volatility returns and should be considered when addressing investment decisions