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Volatility forecasts embedded in the prices of crude‐oil options

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  • Dudley Gilder
  • Leonidas Tsiaras

Abstract

This paper evaluates the ability of alternative option‐implied volatility measures to forecast crude‐oil return volatility. We find that a corridor implied volatility measure that aggregates information from a narrow range of option contracts consistently outperforms forecasts obtained by the popular Black–Scholes and model‐free volatility expectations, as well as those generated by a realized volatility model. This measure ranks favorably in regression‐based tests, delivers the lowest forecast errors under different loss functions, and generates economically significant gains in volatility timing exercises. Our results also show that the Chicago Board Options Exchange's “oil‐VIX” index performs poorly, as it routinely produces the least accurate forecasts.

Suggested Citation

  • Dudley Gilder & Leonidas Tsiaras, 2020. "Volatility forecasts embedded in the prices of crude‐oil options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1127-1159, July.
  • Handle: RePEc:wly:jfutmk:v:40:y:2020:i:7:p:1127-1159
    DOI: 10.1002/fut.22114
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    2. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    3. Feng, Lingbing & Rao, Haicheng & Lucey, Brian & Zhu, Yiying, 2024. "Volatility forecasting on China's oil futures: New evidence from interpretable ensemble boosting trees," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1595-1615.
    4. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2023. "Exploring volatility of crude oil intraday return curves: A functional GARCH-X model," Journal of Commodity Markets, Elsevier, vol. 32(C).

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