Testing Alternative Models for Forecasting Volatility in Stock Futures Market: A Linear Approach

Abstract

Thanks to numerous empirical research studies, a general consensus has been reached on examining the various alternative models for forecasting and modeling volatility in stock futures contracts by using out-of-sample forecast according to statistic and risk management evaluation criteria. The dataset were retrieved from National Stock Exchange (NSE) website terminal for the period from April 1, 2003 and ending on December 31, 2008. The forecasting models that are considered here ranges from Random Walk, Linear Regression, Moving Average, Autoregressive Models. In order to evaluate the forecasting performance of different models we use two forecasting error statistics by considering the root mean square error (RMSE) and the mean absolute percentage error (MAPE) for testing the return characteristics. Our findings suggest that, according to RMSE statistics the autoregressive model and linear regression models rationally shared and ranked first for out-of-sample forecasts in the linear models. In addition, one cannot conclude that the success or failure of a particular type of forecasting model applied to one market carries over to a different market, because the size and liquidity of a market can affect the quality of volatility forecasts. Finally, one can learn more about the market movement and return volatility through studying the non linear approach.