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Mean Reversion Trading on the Naphtha Crack

Author

Listed:
  • Briac Turquet

    (ETH Zurich)

  • Pierre Bajgrowicz

    (Axpo Solutions AG)

  • O. Scaillet

    (Swiss Finance Institute - University of Geneva)

Abstract

We investigate the mean reversion of the naphtha crack after large price moves on daily data over 2014-2024. Our non-parametric estimation of the dynamics of daily changes assuming a univariate diffusion process shows that the reversion strength increases non-linearly after daily moves exceeding a certain threshold. We perform Monte Carlo simulations to study the duration for which the reversion is likely to remain active. We then backtest corresponding trading strategies. We calibrate parameters of the strategy using grid search while controlling for multiple testing. On average the tested strategies deliver positive returns after transaction costs. We are able to select a subset of outperforming strategies generating robust positive net returns. The existence of positive returns can be explained by differences in liquidity, execution speed, and categories of participants in the naphtha and Brent markets constituting the two legs of the naphtha crack.

Suggested Citation

  • Briac Turquet & Pierre Bajgrowicz & O. Scaillet, 2024. "Mean Reversion Trading on the Naphtha Crack," Swiss Finance Institute Research Paper Series 24-101, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp24101
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    References listed on IDEAS

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    1. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    2. Ana Babus & Péter Kondor, 2018. "Trading and Information Diffusion in Over‐the‐Counter Markets," Econometrica, Econometric Society, vol. 86(5), pages 1727-1769, September.
    3. Mahringer, Steffen & Prokopczuk, Marcel, 2015. "An empirical model comparison for valuing crack spread options," Energy Economics, Elsevier, vol. 51(C), pages 177-187.
    4. Chris Brooks & Marcel Prokopczuk, 2013. "The dynamics of commodity prices," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 527-542, March.
    5. Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
    6. Ohana, Steve, 2010. "Modeling global and local dependence in a pair of commodity forward curves with an application to the US natural gas and heating oil markets," Energy Economics, Elsevier, vol. 32(2), pages 373-388, March.
    7. Bellia, Mario & Girardi, Giulio & Panzica, Roberto & Pelizzon, Loriana & Peltonen, Tuomas, 2024. "The demand for central clearing: To clear or not to clear, that is the question!," Journal of Financial Stability, Elsevier, vol. 72(C).
    8. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    9. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    10. Torsten Ehlers & Bryan Hardy, 2019. "The evolution of OTC interest rate derivatives markets," BIS Quarterly Review, Bank for International Settlements, December.
    11. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    12. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. John Hua Fan & Tingxi Zhang, 2024. "Commodity premia and risk management," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1097-1116, July.
    15. Álvaro Cartea & Patrick Chang & Mateusz Mroczka & Roel Oomen, 2022. "AI-driven liquidity provision in OTC financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 22(12), pages 2171-2204, December.
    16. Holmes, Mark J. & Otero, Jesús & Panagiotidis, Theodore, 2013. "On the dynamics of gasoline market integration in the United States: Evidence from a pair-wise approach," Energy Economics, Elsevier, vol. 36(C), pages 503-510.
    17. Taewook Kim & Ha Young Kim, 2019. "Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    18. Ana Babus & Péter Kondor & Yilin Wang, 2020. "Corrigendum to “Trading and Information Diffusion in Over‐the‐Counter Markets”," Econometrica, Econometric Society, vol. 88(5), pages 2221-2228, September.
    19. Stanton, Richard, 1997. "A Nonparametric Model of Term Structure Dynamics and the Market Price of Interest Rate Risk," Journal of Finance, American Finance Association, vol. 52(5), pages 1973-2002, December.
    20. Frino, Alex & Ibikunle, Gbenga & Mollica, Vito & Steffen, Tom, 2018. "The impact of commodity benchmarks on derivatives markets: The case of the dated Brent assessment and Brent futures," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 27-43.
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    Keywords

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    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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