Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models

Abstract

We analyze the predictability of daily data on the CBOE VIX and SKEW indices, used to capture the average level of risk-neutral risk and downside risk, respectively, as implied by S&P 500 index options. In particular, we use forecast models from the Heterogeneous Autoregressive (HAR) class to test whether and how lagged values of the VIX and of the SKEW may increase the forecasting power of HAR for the SKEW and the VIX. We find that a simple HAR is very hard to beat in out-of-sample experiments aimed at forecasting the VIX. In the case of the SKEW, the benchmarks (the random walk and an AR(1)) are clearly outperformed by HAR models at all the forecast horizons considered and there is evidence that special definitions of the SKEW index based on put options data only yield superior forecasts at all horizons.