Combining Stock Market Volatility Forecasts Using an EWMA Technique

Abstract

Forecasting, stock market volatility is an important and challenging task for both academic researchers and business practitioners. The recent trend to improve the prediction accuracy is to combine individual forecasts using a simple average or weighted average where the weight reflects the inverse of the prediction error. In the existing combining methods, however, the errors between actual and predicted values are equally reflected in the weights regardless of the time order in a forecasting horizon. In this paper, we present a new approach where the forecasting results of the generalized autoregressive conditional heteroscedastic (GARCH), the exponential generalized autoregressive conditional heteroscedastic(EGARCH), and random walk models are combined based on a weight that reflects the inverse of the exponentially weighted moving average (EWMA) of the mean absolute percentage error (MAPE) of each individual prediction model. The results of an empirical study indicate that the proposed method has a better accuracy than the generalized autoregressive conditional heteroscedastic (GARCH), exponential generalized autoregressive conditional heteroscedastic (EGARCH) and random walk models, and also combining methods based on using the MAPE for the weight.