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Price co-movement and the crack spread in the US futures markets

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  • Fousekis, Panos
  • Grigoriadis, Vasilis

Abstract

The strength and the pattern of linkages between output and input futures prices are of particular importance for risk management in the energy sector. This paper investigates the co-movement between crude oil, heating oil, and reformulated gasoline futures prices using non-parametric and time-varying copulas. The empirical results suggest that short-run co-movement is high, symmetric with respect to the sign of shocks, and asymmetric with respect to the size of them. Depending on the source of a shock, the asymmetry with respect to size is likely to work towards widening or narrowing the crack spread. In the long run, however, price co-movement becomes perfect, and the price interrelationships obey the Law of One Price.

Suggested Citation

  • Fousekis, Panos & Grigoriadis, Vasilis, 2017. "Price co-movement and the crack spread in the US futures markets," Journal of Commodity Markets, Elsevier, vol. 7(C), pages 57-71.
  • Handle: RePEc:eee:jocoma:v:7:y:2017:i:c:p:57-71
    DOI: 10.1016/j.jcomm.2017.08.003
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    as
    1. Rong Liu & Lijian Yang, 2008. "Kernel estimation of multivariate cumulative distribution function," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(8), pages 661-677.
    2. Panagiotou Dimitrios & Stavrakoudis Athanassios, 2016. "Price Dependence between Different Beef Cuts and Quality Grades: A Copula Approach at the Retail Level for the U.S. Beef Industry," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 14(1), pages 121-131, May.
    3. Goldberg, Pinelopi K. & Verboven, Frank, 2005. "Market integration and convergence to the Law of One Price: evidence from the European car market," Journal of International Economics, Elsevier, vol. 65(1), pages 49-73, January.
    4. Liu, Pan & Vedenov, Dmitry & Power, Gabriel J., 2017. "Is hedging the crack spread no longer all it's cracked up to be?," Energy Economics, Elsevier, vol. 63(C), pages 31-40.
    5. Severin Borenstein & Andrea Shepard, 2002. "Sticky Prices, Inventories, and Market Power in Wholesale Gasoline Markets," RAND Journal of Economics, The RAND Corporation, vol. 33(1), pages 116-139, Spring.
    6. Elena Di Bernardino & Didier Rullière, 2017. "A note on upper-patched generators for Archimedean copulas," Post-Print hal-01347869, HAL.
    7. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    8. Jean-David FERMANIAN & Olivier SCAILLET, 2003. "Nonparametric Estimation of Copulas for Time Series," FAME Research Paper Series rp57, International Center for Financial Asset Management and Engineering.
    9. Jochen Meyer & Stephan von Cramon‐Taubadel, 2004. "Asymmetric Price Transmission: A Survey," Journal of Agricultural Economics, Wiley Blackwell, vol. 55(3), pages 581-611, November.
    10. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
    11. Joe, Harry & Li, Haijun & Nikoloulopoulos, Aristidis K., 2010. "Tail dependence functions and vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 252-270, January.
    12. Cyril Caillault & Dominique Guegan, 2005. "Empirical estimation of tail dependence using copulas: application to Asian markets," Quantitative Finance, Taylor & Francis Journals, vol. 5(5), pages 489-501.
    13. 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.
    14. Fabio Busetti & Andrew Harvey, 2011. "When is a Copula Constant? A Test for Changing Relationships," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 106-131, Winter.
    15. Grasso, Margherita & Manera, Matteo, 2007. "Asymmetric error correction models for the oil-gasoline price relationship," Energy Policy, Elsevier, vol. 35(1), pages 156-177, January.
    16. Silva Filho, Osvaldo Candido da & Ziegelmann, Flavio Augusto & Dueker, Michael J., 2012. "Modeling dependence dynamics through copulas with regime switching," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 346-356.
    17. Asche, Frank & Gjolberg, Ole & Volker, Teresa, 2003. "Price relationships in the petroleum market: an analysis of crude oil and refined product prices," Energy Economics, Elsevier, vol. 25(3), pages 289-301, May.
    18. Koirala, Krishna H. & Mishra, Ashok K. & D'Antoni, Jeremy M. & Mehlhorn, Joey E., 2015. "Energy prices and agricultural commodity prices: Testing correlation using copulas method," Energy, Elsevier, vol. 81(C), pages 430-436.
    19. Douglas, Christopher C., 2010. "Do gasoline prices exhibit asymmetry? Not usually!," Energy Economics, Elsevier, vol. 32(4), pages 918-925, July.
    20. Bremmer, Dale S. & Kesselring, Randall G., 2016. "The relationship between U.S. retail gasoline and crude oil prices during the Great Recession: “Rockets and feathers” or “balloons and rocks” behavior?," Energy Economics, Elsevier, vol. 55(C), pages 200-210.
    21. Xun Lu & Kin Lai & Liang Liang, 2014. "Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model," Annals of Operations Research, Springer, vol. 219(1), pages 333-357, August.
    22. Jeffrey Racine, 2015. "Mixed data kernel copulas," Empirical Economics, Springer, vol. 48(1), pages 37-59, February.
    23. Paul Berhanu Girma & Albert S. Paulson, 1999. "Risk arbitrage opportunities in petroleum futures spreads," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(8), pages 931-955, December.
    24. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.
    25. Jammazi, Rania & Reboredo, Juan C., 2016. "Dependence and risk management in oil and stock markets. A wavelet-copula analysis," Energy, Elsevier, vol. 107(C), pages 866-888.
    26. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    27. Nathan S. Balke & Stephen P. A. Brown & Mine K. Yücel, 1998. "Crude oil and gasoline prices: an asymmetric relationship?," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q 1, pages 2-11.
    28. Fousekis, Panos & Emmanouilides, Christos & Grigoriadis, Vasilis, 2017. "Price linkages in the international skim milk powder market: empirical evidence from nonparametric and time-varying copulas," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 61(1), January.
    29. Jadran Dobric & Friedrich Schmid, 2005. "Nonparametric estimation of the lower tail dependence λL in bivariate copulas," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(4), pages 387-407.
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