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Trading futures spreads: an application of correlation and threshold filters

Author

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  • C. L. Dunis
  • Jason Laws
  • Ben Evans

Abstract

A clear motivation for this paper is the investigation of a correlation filter to improve the return/risk performance of spread trading models. A further motivation for this paper is the extension of trading futures spreads beyond the 'Fair Value' type of model used by Butterworth and Holmes (2002). The trading models tested are the following: the cointegration 'fair value' approach; reverse moving average (of which the results of the 20-day model are shown here); traditional regression techniques; and Neural Network Regression. Also shown is the effectiveness of two types of filter: a standard filter and a correlation filter on the trading rule returns. Results show that the best model for trading the WTI-Brent spread is the MACD model, which proved to be profitable, both in- and out-of-sample. This is evidenced by out-of-sample annualised returns of 26.35% for the standard filter and 26.15% for the correlation filter (inclusive of transactions costs).

Suggested Citation

  • C. L. Dunis & Jason Laws & Ben Evans, 2006. "Trading futures spreads: an application of correlation and threshold filters," Applied Financial Economics, Taylor & Francis Journals, vol. 16(12), pages 903-914.
    • Handle: RePEc:taf:apfiec:v:16:y:2006:i:12:p:903-914
    DOI: 10.1080/09603100500426432
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    References listed on IDEAS

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    Cited by:

    1. Auer, Benjamin R., 2014. "Daily seasonality in crude oil returns and volatilities," Energy Economics, Elsevier, vol. 43(C), pages 82-88.
    2. 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.
    3. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    5. Bianchi, Robert J. & Fan, John Hua & Miffre, Joëlle & Zhang, Tingxi, 2023. "Exploiting the dynamics of commodity futures curves," Journal of Banking & Finance, Elsevier, vol. 154(C).
    6. Viviana Fanelli & Claudio Fontana & Francesco Rotondi, 2023. "A hidden Markov model for statistical arbitrage in international crude oil futures markets," Papers 2309.00875, arXiv.org, revised Sep 2024.
    7. Christian L. Dunis & Jason Laws & Peter W. Middleton & Andreas Karathanasopoulos, 2013. "Nonlinear Forecasting Of The Gold Miner Spread: An Application Of Correlation Filters," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 207-231, October.
    8. Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Higher order and recurrent neural architectures for trading the EUR/USD exchange rate," Quantitative Finance, Taylor & Francis Journals, vol. 11(4), pages 615-629.
    9. Lubnau, Thorben, 2014. "Spread trading strategies in the crude oil futures market," Discussion Papers 353, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    10. John B. Mitchell, 2010. "Soybean Futures Crush Spread Arbitrage: Trading Strategies and Market Efficiency," JRFM, MDPI, vol. 3(1), pages 1-34, December.
    11. Fernando Caneo & Werner Kristjanpoller, 2021. "Improving statistical arbitrage investment strategy: Evidence from Latin American stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4424-4440, July.

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