Forecasting stock volatility with a large set of predictors: A new forecast combination method

Abstract This paper investigates how to improve the prediction accuracy of stock realized volatility using a large set of predictors. Exploiting normalized positive adjusted and significant 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, different dependent variables, and different out‐of‐sample periods. Furthermore, we explain that out‐of‐sample predictability varies significantly with changes in the number of predictors. And the existence of a strongly powerful volatility predictor affects this change.