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A dynamic hedging approach for refineries in multiproduct oil markets

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  • Ji, Qiang
  • Fan, Ying

Abstract

A multiproduct portfolio hedge ratio strategy for oil futures is investigated using a multivariate GARCH model based on dynamic conditional correlation and an error correction model (DCC-ECM-MVGARCH). By considering the characteristics of refiner profits from crack spread and the mutual relations among crude oil, gasoline and heating oil spot and future prices, we estimate the time-varying optimal hedge ratios for the oil-cracking margin. In addition, a naïve strategy, a traditional OLS model and dynamic B-GARCH model are selected to compare with our model for hedge effectiveness. Comparison of hedge effectiveness for in-sample and out-of-sample data reveals that the dynamic DCC-ECM-MVGARCH model is more sensitive to market fluctuations, provides a more accurate description of changes in volatility and has more advantages than other models. Therefore, the empirical results prove that application of the DCC-ECM-MVGARCH model for hedging of oil market portfolio can play an important role in avoiding the double risk of crude oil and oil product markets for refineries.

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  • Ji, Qiang & Fan, Ying, 2011. "A dynamic hedging approach for refineries in multiproduct oil markets," Energy, Elsevier, vol. 36(2), pages 881-887.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:2:p:881-887
    DOI: 10.1016/j.energy.2010.12.025
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    4. Quintino, António & Catalão-Lopes, Margarida & Lourenço, João Carlos, 2019. "Can switching from gasoline to aromatics mitigate the price risk of refineries?," Energy Policy, Elsevier, vol. 134(C).
    5. Cuilin Li & Ya-Juan Du & Qiang Ji & Jiang-bo Geng, 2019. "Multiscale Market Integration and Nonlinear Granger Causality between Natural Gas Futures and Physical Markets," Sustainability, MDPI, vol. 11(19), pages 1-23, October.
    6. You-How Go & Wee-Yeap Lau, 2019. "Palm oil spot-futures relation: Evidence from unrefined and refined products," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 65(3), pages 133-142.
    7. Alexander, Carol & Prokopczuk, Marcel & Sumawong, Anannit, 2013. "The (de)merits of minimum-variance hedging: Application to the crack spread," Energy Economics, Elsevier, vol. 36(C), pages 698-707.
    8. Čech, František & Zítek, Michal, 2022. "Marine fuel hedging under the sulfur cap regulations," Energy Economics, Elsevier, vol. 113(C).
    9. Krzysztof Osiewalski & Jacek Osiewalski, 2013. "A Long-Run Relationship between Daily Prices on Two Markets: The Bayesian VAR(2)–MSF-SBEKK Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(1), pages 65-83, March.
    10. Vedenov, Dmitry & Power, Gabriel J., 2022. "We don't need no fancy hedges! Or do we?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    11. George E. Halkos & Apostolos S. Tsirivis, 2019. "Energy Commodities: A Review of Optimal Hedging Strategies," Energies, MDPI, vol. 12(20), pages 1-19, October.
    12. Sukcharoen, Kunlapath & Leatham, David J., 2017. "Hedging downside risk of oil refineries: A vine copula approach," Energy Economics, Elsevier, vol. 66(C), pages 493-507.
    13. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.

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