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Forecasting market index volatility using Ross-recovered distributions

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  • Marie-Hélène Gagnon
  • Gabriel J. Power
  • Dominique Toupin

Abstract

The Ross recovery theorem shows that option data can reveal the market’s true (physical) expectations. We adapt this approach to international index options data (S&P, FTSE, CAC, SMI, and DAX) to improve volatility forecasting. We separate implied volatility into Ross-recovered expected volatility and a risk preference proxy. We investigate the performance of these variables, constructed domestically or globally, to forecast realized volatility as well as index excess returns. The results show evidence of significantly improved forecasts and yield new insights on the international dynamics of risk expectations and preferences. Across indexes, models using Ross-recovered, value-weighted global measures of risk preferences perform best. The findings suggest that the recovery theorem is empirically useful.

Suggested Citation

  • Marie-Hélène Gagnon & Gabriel J. Power & Dominique Toupin, 2022. "Forecasting market index volatility using Ross-recovered distributions," Quantitative Finance, Taylor & Francis Journals, vol. 22(2), pages 255-271, February.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:2:p:255-271
    DOI: 10.1080/14697688.2021.1939407
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