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Forecasting oil price movements with crack spread futures

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  • Murat, Atilim
  • Tokat, Ekin
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    Abstract

    In oil markets, the crack spread refers to the crude-product price relationship. Refiners are major participants in oil markets and they are primarily exposed to the crack spread. In other words, refiner activity is substantially driven by the objective of protecting the crack spread. Moreover, oil consumers are active participants in the oil hedging market and they are frequently exposed to the crack spread. From another perspective, hedge funds are heavily using crack spread to speculate in oil markets. Based on the high volume of crack spread futures trading in oil markets, the question we want to raise is whether the crack spread futures can be a good predictor of oil price movements. We investigated first whether there is a causal relationship between the crack spread futures and the spot oil markets in a vector error correction framework. We found the causal impact of crack spread futures on spot oil market both in the long- and the short-run after April 2003 where we detected a structural break in the model. To examine the forecasting performance, we use the random walk model (RWM) as a benchmark, and we also evaluate the forecasting power of crack spread futures against the crude oil futures. The results showed that (a) both the crack spread futures and the crude oil futures outperformed the RWM; and (b) the crack spread futures are almost as good as the crude oil futures in predicting the movements in spot oil markets.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 31 (2009)
    Issue (Month): 1 (January)
    Pages: 85-90

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    Handle: RePEc:eee:eneeco:v:31:y:2009:i:1:p:85-90

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Oil prices Crack spread Vector error correction;

    References

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    1. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
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    Cited by:
    1. Ghaffari, Ali & Zare, Samaneh, 2009. "A novel algorithm for prediction of crude oil price variation based on soft computing," Energy Economics, Elsevier, vol. 31(4), pages 531-536, July.
    2. Meyler, Aidan, 2009. "The pass through of oil prices into euro area consumer liquid fuel prices in an environment of high and volatile oil prices," Energy Economics, Elsevier, vol. 31(6), pages 867-881, November.
    3. Westgaard, Sjur & Estenstad, Maria & Seim, Maria & Frydenberg, Stein, 2011. "Co-integration of ICE Gas oil and Crude oil futures," Energy Economics, Elsevier, vol. 33(2), pages 311-320, March.
    4. Al-mulali, Usama & Che Sab, Normee, 2009. "The Impact of Oil Prices on the Real Exchange Rate of the Dirham: a Case Study of the United Arab Emirates," MPRA Paper 23493, University Library of Munich, Germany.
    5. Giliola Frey & Matteo Manera & Anil Markandya & Elisa Scarpa, 2009. "Econometric Models for Oil Price Forecasting: A Critical Survey," CESifo Forum, Ifo Institute for Economic Research at the University of Munich, vol. 10(1), pages 29-44, 04.
    6. Baumeister, Christiane & Kilian, Lutz & Zhou, Xiaoqing, 2013. "Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis," CEPR Discussion Papers 9572, C.E.P.R. Discussion Papers.
    7. Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.
    8. Chen, Shyh-Wei & Lin, Shih-Mo, 2014. "Non-linear dynamics in international resource markets: Evidence from regime switching approach," Research in International Business and Finance, Elsevier, vol. 30(C), pages 233-247.
    9. Wang, Yudong & Wu, Chongfeng, 2012. "What can we learn from the history of gasoline crack spreads?: Long memory, structural breaks and modeling implications," Economic Modelling, Elsevier, vol. 29(2), pages 349-360.

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