Forecasting VIX with time-varying risk aversion

Xinyu Wu, Qizhi He and Haibin Xie

International Review of Economics & Finance, 2023, vol. 88, issue C, 458-475

Abstract: In this paper, we investigate the predictive value of time-varying risk aversion (RA) for VIX via the realized EGARCH-mixed-data sampling model incorporating RA (henceforth REGARCH-MIDAS-RA). The REGARCH-MIDAS-RA model builds on the REGARCH model, which takes into account the high-frequency information by including the realized measure of volatility. Moreover, the model provides a convenient framework to model the long-run variance, which responds to changes in RA. We obtain the risk-neutralization of the REGARCH-MIDAS-RA model and derive the model-implied VIX formula. Our empirical results show that realized measure and RA possess predictive value for VIX. The REGARCH-MIDAS-RA model yields more accurate VIX forecasts compared to a range of competing models, including the GARCH, GJR-GARCH, nonlinear GARCH, EGARCH, REGARCH and REGARCH-MIDAS. In summary, our findings highlight the importance of incorporating the realized measure as well as RA in forecasting VIX.

Keywords: VIX forecasting; Time-varying risk aversion; Realized EGARCH; Mixed data sampling; Realized measure (search for similar items in EconPapers)
JEL-codes: C22 C53 G13 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:88:y:2023:i:c:p:458-475

DOI: 10.1016/j.iref.2023.06.034

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