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Empirical Minimum Variance Hedge, The

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Abstract

Decision making under unknown true parameters (estimation risk) is discussed along with Bayes and parameter certainty equivalent (PCE) criteria. Bayes criterion provides the solution for optimal decision making under estimation risk in a manner consistent with expected utility maximization. The PCE method is not consistent with expected utility maximization, but is the approach commonly used. Bayes criterion is applied to solve for the minimum variance hedge ratio (MVH) in two scenarios based on the multivariate normal distribution. Simulations show that discrepancies between prior and sample parameters may lead to substantial differences between Bayesian and PCE MVHs. Such discrepancies also highlight the superiority of Bayes criterion over the PCE, in the sense that the PCE method cannot not yield decision rules that contain prior (or nonsample) along with sample information.

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  • Sergio H. Lence & Dermot J. Hayes, 1993. "Empirical Minimum Variance Hedge, The," Center for Agricultural and Rural Development (CARD) Publications 93-wp109, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    • Handle: RePEc:ias:cpaper:93-wp109
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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. Pautsch, Gregory R. & Babcock, Bruce A. & Breidt, F. Jay, 1999. "Optimal Information Acquisition Under A Geostatistical Model," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 24(2), pages 1-25, December.
    3. 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.
    4. Ahmad Bash & Abdullah M. Al-Awadhi & Fouad Jamaani, 2016. "Measuring the Hedge Ratio: A GCC Perspective," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(7), pages 1-1, July.
    5. repec:dgr:rugsom:96b28 is not listed on IDEAS
    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. Chen, Ren-Raw & Leistikow, Dean & Wang, Andrew, 2020. "Futures minimum variance hedge ratio determination: An ex-ante analysis," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Muus, L. & Scheer, H. van der & Wansbeek, T., 1996. "A decision theoretic framework for profit maximization in direct marketing," Research Report 96B28, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    10. Jonathan Dark, 2005. "A Critique of Minimum Variance Hedging," Accounting Research Journal, Emerald Group Publishing, vol. 18(1), pages 40-49, June.
    11. Wilson, William W. & Wagner, Robert & Nganje, William E., 2003. "Strategic Hedging For Grain Processors," Agribusiness & Applied Economics Report 23637, North Dakota State University, Department of Agribusiness and Applied Economics.
    12. Songjiao Chen & William Wilson & Ryan Larsen & Bruce Dahl, 2016. "Risk Management for Grain Processors and “Copulas”," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(2), pages 365-382, June.
    13. Dorfman, Jeffrey H. & Sanders, Dwight R., 2004. "Generalized Hedge Ratio Estimation With An Unknown Model," 2004 Conference, April 19-20, 2004, St. Louis, Missouri 19024, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    14. Andreas Röthig, 2009. "Microeconomic Risk Management and Macroeconomic Stability," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-01565-6, March.
    15. 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.
    16. 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.
    17. Mason, Charles Edwin, IV, 2000. "Estimation and attenuation of reinsurance risk in the crop insurance market," ISU General Staff Papers 2000010108000013703, Iowa State University, Department of Economics.
    18. Pautsch, Gregory R. & Babcock, Bruce A. & Breidt, F. Jay, 1998. "Optimal Sampling Under a Geostatistical Model," Hebrew University of Jerusalem Archive 18424, Hebrew University of Jerusalem.

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