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Implied volatility in oil markets

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  • Borovkova, Svetlana
  • Permana, Ferry J.

Abstract

Modelling the implied volatility surface as a function of an option's strike price and maturity is a subject of extensive research in financial markets. The implied volatility in commodity markets is much less studied, due to a limited liquidity and the complicated structure of commodity options. A new semi-parametric method is introduced for modelling the implied volatility surface and is applied to the option price data from oil markets. This approach combines the simplicity of a parametric method with the flexibility of a non-parametric approach. The method can successfully deal with a limited amount of option price data. Performance of the method is investigated by applying it to prices of exchange-traded crude oil and gasoline options, and the results are compared with those obtained by a purely parametric approach. Furthermore, the investigation of the relationship between volatilities implied from European and Asian options shows that Asian options in oil markets are significantly more expensive than theoretical arguments imply.

Suggested Citation

  • Borovkova, Svetlana & Permana, Ferry J., 2009. "Implied volatility in oil markets," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2022-2039, April.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:6:p:2022-2039
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    Cited by:

    1. Chang, C-L. & Hsieh, T-L. & McAleer, M.J., 2016. "How are VIX and Stock Index ETF Related?," Econometric Institute Research Papers EI2016-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Papers 1107.1834, arXiv.org.
    3. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "Connecting VIX and Stock Index ETF," Tinbergen Institute Discussion Papers 16-010/III, Tinbergen Institute, revised 23 Jan 2017.
    4. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
    5. Liu, Zhenhua & Tseng, Hui-Kuan & Wu, Jy S. & Ding, Zhihua, 2020. "Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects," Resources Policy, Elsevier, vol. 66(C).
    6. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2018. "Connecting VIX and Stock Index ETF with VAR and Diagonal BEKK," JRFM, MDPI, vol. 11(4), pages 1-25, September.
    7. Śmiech, Sławomir & Papież, Monika & Rubaszek, Michał & Snarska, Małgorzata, 2021. "The role of oil price uncertainty shocks on oil-exporting countries," Energy Economics, Elsevier, vol. 93(C).
    8. Itkin, Andrey, 2015. "To sigmoid-based functional description of the volatility smile," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 264-291.
    9. Zdeněk Drábek & Miloš Kopa & Matúš Maciak & Michal Pešta & Sebastiano Vitali, 2023. "Investment disputes and their explicit role in option market uncertainty and overall risk instability," Computational Management Science, Springer, vol. 20(1), pages 1-25, December.
    10. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    11. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.

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