Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models

We analyze the predictability of daily data on the CBOE V I X and S K E W 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 ( H A R ) class to test whether and how lagged values of the V I X and of the S K E W may increase the forecasting power of H A R for the S K E W and the V I X . We find that a simple H A R is very hard to beat in out-of-sample experiments aimed at forecasting the V I X . In the case of the S K E W , the benchmarks (the random walk and an A R ( 1 ) ) are clearly outperformed by H A R models at all the forecast horizons considered and there is evidence that special definitions of the S K E W index based on put options data only yield superior forecasts at all horizons.