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Trading on mean-reversion in energy futures markets

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  • Lubnau, Thorben
  • Todorova, Neda

Abstract

We study whether simple technical trading strategies enjoying large popularity among practitioners can be employed profitably in the context of hedge portfolios for Crude Oil, Natural Gas, Gasoline and Heating Oil futures. The strategies tested are based on mean-reverting calendar spread portfolios established with dynamic hedge ratios. Entry and exit signals are generated by so-called Bollinger Bands. The trading system is applied to twenty-two years of historical data from 1992 to 2013 for various specifications, taking transaction costs into account. The significance of the results is evaluated with a bootstrap test in which randomly generated orders are compared to orders placed by the trading system. Whereas we find most combinations involving the front-month and second-month futures to be significantly profitable for all commodities tested, the best results for the risk-adjusted Sharpe Ratio are obtained for WTI Crude Oil and Natural Gas, with Sharpe Ratios in excess of 2 for most combinations and a rather smooth performance for all calendar spreads. Based on our results, there is a serious doubt whether energy futures markets can be considered weakly efficient in the short-term.

Suggested Citation

  • Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    • Handle: RePEc:eee:eneeco:v:51:y:2015:i:c:p:312-319
    DOI: 10.1016/j.eneco.2015.06.018
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    2. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers 1907.09452, arXiv.org.
    3. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    4. Helder Sebastião & Pedro Godinho & Sjur Westgaard, 2020. "Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(4), pages 1-17, December.
    5. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
    6. Day, Min-Yuh & Ni, Yensen, 2023. "Do clean energy indices outperform using contrarian strategies based on contrarian trading rules?," Energy, Elsevier, vol. 272(C).
    7. Adam Zaremba, 2019. "The Cross Section of Country Equity Returns: A Review of Empirical Literature," JRFM, MDPI, vol. 12(4), pages 1-26, October.
    8. Alexopoulos, Thomas A., 2018. "To trust or not to trust? A comparative study of conventional and clean energy exchange-traded funds," Energy Economics, Elsevier, vol. 72(C), pages 97-107.
    9. Phan, Dinh Hoang Bach & Narayan, Paresh Kumar & Gong, Qiang, 2021. "Terrorist attacks and oil prices: Hypothesis and empirical evidence," International Review of Financial Analysis, Elsevier, vol. 74(C).
    10. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, July.
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    14. Johannes Stübinger & Lucas Schneider, 2019. "Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500," JRFM, MDPI, vol. 12(2), pages 1-19, April.
    15. Hsu, Chih-Hsiang, 2021. "The predictability of the return correlation of futures with different expirations in the Chinese futures market," Resources Policy, Elsevier, vol. 74(C).
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    19. Taylor, Nick, 2017. "Timing strategy performance in the crude oil futures market," Energy Economics, Elsevier, vol. 66(C), pages 480-492.

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    More about this item

    Keywords

    Technical trading; Bollinger Bands; Energy futures; Market efficiency;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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