The role of model bias in predicting volatility: evidence from the US equity markets

Abstract

Purpose The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility. Design/methodology/approach Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively. Findings The in-sample results reveal that the prediction model including the model bias can obtain bigger R 2 , and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models. Originality/value The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.