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The Long-Term Structure of Commodity Futures

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  • Na Jin
  • Sergio Lence
  • Chad Hart
  • Dermot Hayes

Abstract

Futures markets on agricultural commodities typically trade with maximum maturity dates of less than four years. If these markets did trade with maturities eight or ten years distant, futures prices would have value as price forecasts and as a way to structure long-term swaps and insurance contracts. Agricultural commodity markets generally exhibit mean reversion in spot prices and convenience yields. Spot markets also exhibit seasonality. This study develops and implements a procedure to generate long-term futures curves from existing futures prices. Data on lean hogs and soybeans are used to show that the method provides plausible results. Copyright 2012, Oxford University Press.

Suggested Citation

  • Na Jin & Sergio Lence & Chad Hart & Dermot Hayes, 2012. "The Long-Term Structure of Commodity Futures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(3), pages 718-735.
    • Handle: RePEc:oup:ajagec:v:94:y:2012:i:3:p:718-735
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    Cited by:

    1. Zhu, Xiaohong, 2016. "New models to estimate costs of US farm programs," ISU General Staff Papers 201601010800006209, Iowa State University, Department of Economics.
    2. Chad E. Hart & Sergio H. Lence & Dermot J. Hayes & Na Jin, 2016. "Price Mean Reversion, Seasonality, and Options Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(3), pages 707-725.
    3. Zhou, Wei, 2015. "Three essays on modeling biofuel feedstock supply," ISU General Staff Papers 201501010800005728, Iowa State University, Department of Economics.
    4. Li, Lisha, 2015. "Three essays on crop yield, crop insurance and climate change," ISU General Staff Papers 201501010800005371, Iowa State University, Department of Economics.
    5. Delbridge, Timothy A. & King, Robert P., 2016. "Transitioning to Organic Crop Production: A Dynamic Programming Approach," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(3), pages 1-18, September.
    6. Zhou, Wei & Babcock, Bruce A., 2014. "Endogenous Price in a Dynamic Model for Agricultural Supply Analysis," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170584, Agricultural and Applied Economics Association.
    7. Zhu, Xiaohong, 2016. "New models to estimate costs of US farm programs," ISU General Staff Papers 3547, Iowa State University, Department of Economics.
    8. Shao, Chengwu & Bhar, Ramaprasad & Colwell, David B., 2015. "A multi-factor model with time-varying and seasonal risk premiums for the natural gas market," Energy Economics, Elsevier, vol. 50(C), pages 207-214.
    9. Yang, Linghubo & Zhang, Dongxiang, 2013. "Can futures price be a powerful predictor? Frequency domain analysis on Chinese commodity market," Economic Modelling, Elsevier, vol. 35(C), pages 264-271.

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