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Predicting the VIX and the Volatility Risk Premium: What's Credit and Commodity Volatility Risk Got To Do With It?

Author

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  • Ghysels, Eric
  • Andreou, Elena

Abstract

This paper presents an innovative approach to extracting factors which are shown to predict the VIX, the S&P 500 Realized Volatility and the Variance Risk Premium. The approach is innovative along two different dimensions, namely: (1) we extract factors from panels of filtered volatilities - in particular large panels of univariate financial asset ARCH-type models and (2) we price equity volatility risk using factors which go beyond the equity class. These are volatility factors extracted from panels of volatilities of short-term funding and long-run corporate spreads as well as volatilities of energy and metals commodities returns and sport/future spreads.

Suggested Citation

  • Ghysels, Eric & Andreou, Elena, 2014. "Predicting the VIX and the Volatility Risk Premium: What's Credit and Commodity Volatility Risk Got To Do With It?," CEPR Discussion Papers 10236, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:10236
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    References listed on IDEAS

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    1. Anderson, Heather M. & Vahid, Farshid, 2007. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 76-90, January.
    2. Adrian, Tobias & Shin, Hyun Song, 2010. "Liquidity and leverage," Journal of Financial Intermediation, Elsevier, vol. 19(3), pages 418-437, July.
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    2. Michel A. Robe & Jonathan Wallen, 2016. "Fundamentals, Derivatives Market Information and Oil Price Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(4), pages 317-344, April.

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    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets

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