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Hedging downside risk of oil refineries: A vine copula approach

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  • Sukcharoen, Kunlapath
  • Leatham, David J.

Abstract

The financial health of an oil refinery greatly depends on its refining margin or the difference between the prices of its refined products (typically, gasoline and heating oil) and the cost of crude oil. The refinery may hedge against the downside risk of unfavorable price movements using crude oil, gasoline, and heating oil futures. This paper examines the use of a vine copula approach to estimate multiproduct hedge ratios that minimize the downside risk of the refinery. The advantage of the vine copula approach is that it allows us to capture important characteristics of petroleum price changes, including skewness and fat-tailedness in the marginal distributions of individual price change series as well as heterogeneous (tail) dependence patterns between different pairs of price changes. The out-of-sample hedging effectiveness of two popular classes of vine copula models – canonical (C-) and drawable (D-) vine copula models – are evaluated and compared with that of the widely used nonparametric method and three standard multivariate copula models. The empirical results reveal that the D-vine copula model is a good and safe choice in managing the downside risk of the refinery.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:eneeco:v:66:y:2017:i:c:p:493-507
    DOI: 10.1016/j.eneco.2017.07.012
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    2. Dai, Xingyu & Wang, Qunwei & Zha, Donglan & Zhou, Dequn, 2020. "Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach," Energy Economics, Elsevier, vol. 88(C).
    3. Arunanondchai, Panit & Sukcharoen, Kunlapath & Leatham, David J., 2020. "Dealing with tail risk in energy commodity markets: Futures contracts versus exchange-traded funds," Journal of Commodity Markets, Elsevier, vol. 20(C).
    4. Maziar Sahamkhadam & Andreas Stephan, 2019. "Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for the financial crisis," Papers 1912.10328, arXiv.org.
    5. Wang, Yudong & Geng, Qianjie & Meng, Fanyi, 2019. "Futures hedging in crude oil markets: A comparison between minimum-variance and minimum-risk frameworks," Energy, Elsevier, vol. 181(C), pages 815-826.
    6. Wang, Shuang & Wallace, Stein W. & Lu, Jing & Gu, Yewen, 2020. "Handling financial risks in crude oil imports: Taking into account crude oil prices as well as country and transportation risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    7. Hamed Ghoddusi & Franz Wirl, 2019. "A Risk-Hedging View to Refinery Capacity Investment," Working Papers 1327, Economic Research Forum, revised 21 Aug 2019.
    8. Kumar, Satish & Tiwari, Aviral Kumar & Chauhan, Yogesh & Ji, Qiang, 2019. "Dependence structure between the BRICS foreign exchange and stock markets using the dependence-switching copula approach," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 273-284.
    9. Xie, Nan & Wang, Zongrun & Chen, Sicen & Gong, Xu, 2019. "Forecasting downside risk in China’s stock market based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 530-541.
    10. Wen-Chung Hsu & Hsiang-Tai Lee, 2018. "Cross Hedging Stock Sector Risk with Index Futures by Considering the Global Equity Systematic Risk," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 6(2), pages 1-17, April.
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    More about this item

    Keywords

    Downside risk; Energy hedging; Futures hedging; Multiproduct hedging; Vine copula;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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