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Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

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  • Viviana Fanelli

    (Department of Economics, Management and Business Law, University of Bari, Largo Abbazia Santa Scolastica 53, 70124 Bari, Italy)

Abstract

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.

Suggested Citation

  • Viviana Fanelli, 2024. "Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets," Risks, MDPI, vol. 12(7), pages 1-19, June.
  • Handle: RePEc:gam:jrisks:v:12:y:2024:i:7:p:106-:d:1421417
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    References listed on IDEAS

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    1. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    2. Robert B. Barsky & Lutz Kilian, 2004. "Oil and the Macroeconomy Since the 1970s," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 115-134, Fall.
    3. Poutré, Cédric & Dionne, Georges & Yergeau, Gabriel, 2023. "International high-frequency arbitrage for cross-listed stocks," International Review of Financial Analysis, Elsevier, vol. 89(C).
    4. Cochrane, John H, 1988. "How Big Is the Random Walk in GNP?," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 893-920, October.
    5. Szymon Wlazlowski & Bjorn Hagstromer & Monica Giulietti, 2011. "Causality in crude oil prices," Applied Economics, Taylor & Francis Journals, vol. 43(24), pages 3337-3347.
    6. Roy Cerqueti & Viviana Fanelli, 2021. "Long memory and crude oil’s price predictability," Annals of Operations Research, Springer, vol. 299(1), pages 895-906, April.
    7. Jensen, Michael C., 1978. "Some anomalous evidence regarding market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 95-101.
    8. Cerqueti, Roy & Fanelli, Viviana & Rotundo, Giulia, 2019. "Long run analysis of crude oil portfolios," Energy Economics, Elsevier, vol. 79(C), pages 183-205.
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