Modeling and forecasting stock return volatility using the HARGARCH model with VIX information

Abstract

This study develops a novel approach for improving stock return volatility forecasts using volatility index information with the entropic tilting technique. Unlike traditional linear heteroskedasticity autoregressive methods with option-implied information, we first derive predictive densities from traditional models, and then tilt using both the first and second moments of the risk-neutral distribution, which enables us to capture the nonlinear effect in our specification. The empirical findings demonstrate a substantial enhancement in the forecasting accuracy of all models once the first- and second-moment information is considered, where the improvement is both statistically and economically significant. These results have important implications for risk management in well-established derivatives markets.