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The soybean crush spread: Empirical evidence and trading strategies

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  • David P. Simon

Abstract

This article finds that deviations of the soybean crush spread from its long‐run equilibrium were transitory during the sample period from January 1985 through February 1995. This equilibrium is characterized by strong seasonality and by a persistent uptrend in soymeal and soyoil prices relative to soybean prices. A tendency also exists for the crush spread to revert toward its most recent 5‐day average. Simulations demonstrate that trading rules based on these results would have been profitable. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 271–289, 1999

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  • David P. Simon, 1999. "The soybean crush spread: Empirical evidence and trading strategies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(3), pages 271-289, May.
    • Handle: RePEc:wly:jfutmk:v:19:y:1999:i:3:p:271-289
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    Cited by:

    1. Hussein Abdoh & Michael Chitavi, 2024. "The impact of deviations from soybean product crushing estimates on return and risk," Agricultural Economics, International Association of Agricultural Economists, vol. 55(2), pages 181-199, March.
    2. Yang, Yao & Karali, Berna, 2022. "How far is too far for volatility transmission?," Journal of Commodity Markets, Elsevier, vol. 26(C).
    3. Marianna Brunetti & Roberta De Luca, 2023. "Pre-selection in cointegration-based pairs trading," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1611-1640, December.
    4. Sisi Qin & Wee‐Yeap Lau, 2023. "Cross‐border and cross‐commodity volatility spillover effects of Chinese soybean futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1836-1852, December.
    5. Gianfreda, Angelica & Maranzano, Paolo & Parisio, Lucia & Pelagatti, Matteo, 2023. "Testing for integration and cointegration when time series are observed with noise," Economic Modelling, Elsevier, vol. 125(C).
    6. 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.
    7. Ziran Li & Dermot J. Hayes, 2022. "The hedging pressure hypothesis and the risk premium in the soybean reverse crush spread," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(3), pages 428-445, March.
    8. Liu, Jianhe & Lu, Luze & Zong, Xiangyu & Xie, Baao, 2023. "Nonlinear relationships in soybean commodities Pairs trading-test by deep reinforcement learning," Finance Research Letters, Elsevier, vol. 58(PC).
    9. Fousekis, Panos & Tzaferi, Dimitra, 2022. "Price multifractality and informational efficiency in the futures markets of the US soybean complex," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 68-84.
    10. Fabian Waldow & Matthias Schnaubelt & Christopher Krauss & Thomas Günter Fischer, 2021. "Machine Learning in Futures Markets," JRFM, MDPI, vol. 14(3), pages 1-14, March.
    11. Plato, Gerald E., 2001. "The Soybean Processing Decision: Exercising A Real Option On Processing Margins," Technical Bulletins 33567, United States Department of Agriculture, Economic Research Service.
    12. Ruan, Qingsong & Cui, Hao & Fan, Liming, 2020. "China’s soybean crush spread: Nonlinear analysis based on MF-DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    13. Zhou, Xinquan & Bagnarosa, Guillaume & Gohin, Alexandre & Pennings, Joost M.E. & Debie, Philippe, 2023. "Microstructure and high-frequency price discovery in the soybean complex," Journal of Commodity Markets, Elsevier, vol. 30(C).

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