Forecasting Stock Volatility with a Large Set of Predictors : A New Forecast Combination Method

This paper investigates how to improve prediction accuracy of stock realized volatility using a large set of predictors. Exploiting normalized positive adjusted R-square and significant t statistic of predictor obtained from the in-sample result as weight, we develop two simple and effective forecast combination methods. Using an array of 86 equity, bond, forex, futures, behavior, macroeconomic, and uncertainty predictors, we find that the proposed methodologies significantly improve stock realized volatility out-of-sample prediction performance relative to several extant forecast combinations. This result is robust for different individual forecast models, for different dependent variables, and for different out-of-sample periods. Furthermore, we explain that out-of-sample predictability varies significantly with the changes in number of predictors. And the existence of a strongly powerful volatility predictor affects this change