Forecasting stock return volatility using a robust regression model

This paper aims to accurately forecast stock return volatility based on a robust regression model. The robust regression model is developed by replacing the mean squared error (MSE) in the autoregressive (AR) model with the Huber loss function, and the resulting model is called the ARH model. The empirical results show that the ARH model displays significantly stronger predictive power than the AR benchmark model for different evaluation periods and forecasting horizons. From an asset allocation perspective, a mean-variance investor can obtain sizeable utility gains based on the volatility forecasts produced by the ARH model. Furthermore, we find that the superior performance of the ARH model comes from assigning small weights for the extreme values, which are mainly found during recessions and periods of high volatility. Finally, our results are robust to various settings.