Forecasting realized volatility: New evidence from time-varying jumps in VIX

Abstract

Given that jumps in the implied volatility index (VIX) lead to rapid changes in the level of volatility, they may contain significant predictive information for the realized variance (RV) of stock returns. Against this backdrop, the present study proposes to extend the heterogeneous autoregressive (HAR) model using the information content of time-varying jumps occurring in VIX. We find that jumps in VIX have positive impacts on the RV of S&P 500 index and that the proposed HAR-RV approach generates more accurate volatility forecasts than do the existing HAR-RV type models. Importantly, these results hold for short-, medium-, and long-term volatility components.