Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models

Massimo Guidolin and Giulia F. Panzeri
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Giulia F. Panzeri: BAFFI CAREFIN Centre, Bocconi University, 21100 Milan, Italy

Forecasting, 2024, vol. 6, issue 3, 1-33

Abstract: 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.

Keywords: VIX index; SKEW index; Heterogeneous Autoregressive model; superior predictive accuracy (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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