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WTI crude oil option implied VaR and CVaR: An empirical application

Author

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  • Giovanni Barone‐Adesi
  • Marinela Adriana Finta
  • Chiara Legnazzi
  • Carlo Sala

Abstract

Using option market data we derive naturally forward‐looking, nonparametric and model‐free risk estimates, three desired characteristics hardly obtainable using historical returns. The option‐implied measures are only based on the first derivative of the option price with respect to the strike price, bypassing the difficult task of estimating the tail of the return distribution. We estimate and backtest the 1%, 2.5%, and 5% WTI crude oil futures option‐implied value at risk and conditional value at risk for the turbulent years 2011–2016 and for both tails of the distribution. Compared with risk estimations based on the filtered historical simulation methodology, our results show that the option‐implied risk metrics are valid alternatives to the statistically based historical models.

Suggested Citation

  • Giovanni Barone‐Adesi & Marinela Adriana Finta & Chiara Legnazzi & Carlo Sala, 2019. "WTI crude oil option implied VaR and CVaR: An empirical application," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 552-563, September.
    • Handle: RePEc:wly:jforec:v:38:y:2019:i:6:p:552-563
    DOI: 10.1002/for.2580
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    Cited by:

    1. Yiwen Cui & Lei Li & Zijie Tang, 2021. "Risk Analysis of China Stock Market During Economic Downturns–Based on GARCH-VaR and Wavelet Transformation Approaches," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 11(4), pages 322-336, April.
    2. 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.
    3. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
    4. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," LSE Research Online Documents on Economics 119289, London School of Economics and Political Science, LSE Library.
    5. Fabio Bellini & Edit Rroji & Carlo Sala, 2022. "Implicit quantiles and expectiles," Annals of Operations Research, Springer, vol. 313(2), pages 733-753, June.
    6. Giovanni Barone‐Adesi & Chiara Legnazzi & Carlo Sala, 2019. "Option‐implied risk measures: An empirical examination on the S&P 500 index," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1409-1428, October.
    7. Bei, Shuhua & Yang, Aijun & Pei, Haotian & Si, Xiaoli, 2023. "Price Risk Analysis using GARCH Family Models: Evidence from Shanghai Crude Oil Futures Market," Economic Modelling, Elsevier, vol. 125(C).
    8. 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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