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Forecasting value at risk and conditional value at risk using option market data

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  • Annalisa Molino
  • Carlo Sala

Abstract

We forecast monthly value at risk (VaR) and conditional value at risk (CVaR) using option market data and four different econometric techniques. Independent from the econometric approach used, all models produce quick to estimate forward‐looking risk measures that do not depend from the amount of historical data used and that, through the implied moments of options, better reflect the ever‐changing market scenario. All proposed option‐based approaches outperform or are equally good to different “traditional” forecasts that use historical returns as input. The extensive robustness of our results shows that the real driver of the better forecasts is the use of option market data as inputs for the analysis, more than the type of econometric approach implemented.

Suggested Citation

  • Annalisa Molino & Carlo Sala, 2021. "Forecasting value at risk and conditional value at risk using option market data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1190-1213, November.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:7:p:1190-1213
    DOI: 10.1002/for.2756
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    Cited by:

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    2. Zhang, Ning & Su, Xiaoman & Qi, Shuyuan, 2023. "An empirical investigation of multiperiod tail risk forecasting models," International Review of Financial Analysis, Elsevier, vol. 86(C).
    3. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).

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