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Parameter-Based Decision Making Under Estimation Risk: An Application to Futures Trading

Author

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  • Lence, Sergio H.
  • Hayes, Dermot J.

Abstract

This study shows how the standard portfolio model of futures trading should be modified when there is less than perfect information about the relevant parameters (estimation risk). The standard and the optimal decision rules for futures trading in the presence of estimation risk are compared and discussed. An operational model of futures trading for use under estimation risk is advanced. In the presence of relevant prior and sample information, the model can be used to optimally blend both types of information. Copyright 1994 by American Finance Association.
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Suggested Citation

  • Lence, Sergio H. & Hayes, Dermot J., 1994. "Parameter-Based Decision Making Under Estimation Risk: An Application to Futures Trading," Staff General Research Papers Archive 693, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:693
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    Cited by:

    1. Shi, Wei & Irwin, Scott H., 2005. "A Bayesian Implementation of the Standard Optimal Hedging Model: Parameter Estimation Risk and Subjective Views," 2005 Annual meeting, July 24-27, Providence, RI 19155, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Wang, Xuecai & Dorfman, Jeffrey H. & McKissick, John & Turner, Steven C., 2001. "Optimal Marketing Decisions for Feeder Cattle under Price and Production Risk," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 33(3), pages 431-443, December.
    3. Lence, Sergio H. & Hayes, Dermot J., 1995. "Land Allocation In The Presence Of Estimation Risk," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 20(1), pages 1-15, July.
    4. Power, Gabriel J. & Vedenov, Dmitry V., 2008. "The Shape of the Optimal Hedge Ratio: Modeling Joint Spot-Futures Prices using an Empirical Copula-GARCH Model," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37609, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    5. Raphael Markellos & Terence Mills, 2003. "Asset pricing dynamics," The European Journal of Finance, Taylor & Francis Journals, vol. 9(6), pages 533-556.
    6. Bessler, Wolfgang & Leonhardt, Alexander & Wolff, Dominik, 2016. "Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 239-256.
    7. David J. Pannell & Getu Hailu & Alfons Weersink & Amanda Burt, 2008. "More reasons why farmers have so little interest in futures markets," Agricultural Economics, International Association of Agricultural Economists, vol. 39(1), pages 41-50, July.
    8. Rahman, Shaikh Mahfuzur & Dorfman, Jeffrey H. & Turner, Steven C., 2004. "A Bayesian Approach to Optimal Cross-Hedging of Cottonseed Products Using Soybean Complex Futures," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(2), pages 1-16, August.
    9. Wei Shi & Scott H. Irwin, 2005. "Optimal Hedging with a Subjective View: An Empirical Bayesian Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 918-930.
    10. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2018. "Markov switching GARCH models for Bayesian hedging on energy futures markets," Energy Economics, Elsevier, vol. 70(C), pages 545-562.
    11. Zhenyu Cui & Majeed Simaan, 2021. "The opportunity cost of hedging under incomplete information: Evidence from ETF/Ns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1775-1796, November.
    12. Marcos Escobar & Sven Panz, 2016. "A Note on the Impact of Parameter Uncertainty on Barrier Derivatives," Risks, MDPI, vol. 4(4), pages 1-25, September.
    13. Hiroyuki Kashima, 2005. "An application of a minimax Bayes rule and shrinkage estimators to the portofolio selection problem under the Bayesian approach," Statistical Papers, Springer, vol. 46(4), pages 523-540, October.

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