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The Brent-WTI spread revisited: A novel approach

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  • Ruble, Isabella
  • Powell, John

Abstract

This paper takes a novel approach to analyze movements of the Brent-WTI spread over the years 2005 through early 2020. It adds to the existing literature by providing new insights that allow us to better understand to what extent the world crude oil market is truly ‘one great pool’. Our research is based on the premise that the place of physical competition between Brent and WTI is an important factor in determining traders’ actions and therefore the Brent-WTI spread. Changing crude oil flows in the U.S. due to the tight oil revolution together with changes in worldwide demand and supply have changed trade patterns and the location of physical competition between these two crudes. We develop three scenarios using ARIMAX-GARCH and Markov-Switching models. Scenarios 1–3 provide estimates of the impact that variables driving crude oil traders’ decisions have on the Brent-WTI spread. Additionally, we find that the change in the relationship between Brent and WTI that has been observed in the past decade appears to be fundamental, suggesting that the historical premium of WTI to Brent in the futures market is unlikely to return in the near future.

Suggested Citation

  • Ruble, Isabella & Powell, John, 2021. "The Brent-WTI spread revisited: A novel approach," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
  • Handle: RePEc:eee:joecas:v:23:y:2021:i:c:s1703494921000013
    DOI: 10.1016/j.jeca.2021.e00196
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    References listed on IDEAS

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    1. Dragomirescu-Gaina, Catalin & Philippas, Dionisis & Goutte, Stéphane, 2023. "How to ‘Trump’ the energy market: Evidence from the WTI-Brent spread," Energy Policy, Elsevier, vol. 179(C).
    2. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.

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