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Forecasting VIX with time-varying risk aversion

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  • Wu, Xinyu
  • He, Qizhi
  • Xie, Haibin

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.

Suggested Citation

  • Wu, Xinyu & He, Qizhi & Xie, Haibin, 2023. "Forecasting VIX with time-varying risk aversion," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 458-475.
    • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:458-475
    DOI: 10.1016/j.iref.2023.06.034
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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