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Methods for Selecting the Optimal Dynamic Hedge When Production is Stochastic

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  • Larry S. Karp

Abstract

A dynamic hedging problem with stochastic production is formulated and solved. The optimal feedback rules recognize that future hedges will be chosen optimally based on the most current information. The resulting distribution of revenue is analyzed numerically. This information enables the analyst to select the risk aversion parameter that results in the preferred distribution of revenue.

Suggested Citation

  • Larry S. Karp, 1987. "Methods for Selecting the Optimal Dynamic Hedge When Production is Stochastic," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(3), pages 647-657.
    • Handle: RePEc:oup:ajagec:v:69:y:1987:i:3:p:647-657.
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    File URL: http://hdl.handle.net/10.2307/1241699
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    Cited by:

    1. Blank, Steven C., 1989. "Research On Futures Markets: Issues, Approaches, And Empirical Findings," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 14(01), pages 1-14, July.
    2. Monson, Steven J., 1991. "Accounting for yield risk in preharvest commodity pricing decisions," ISU General Staff Papers 1991010108000018169, Iowa State University, Department of Economics.
    3. repec:isu:genstf:1989010108000010138 is not listed on IDEAS
    4. Nyassoke Titi Gaston Clément & Jules Sadefo-Kamdem & Louis Aimé Fono, 2019. "Dynamic Optimal Hedge Ratio Design when Price and Production are stochastic with Jump," Working Papers hal-02417401, HAL.
    5. Harvey Lapan & Giancarlo Moschini & Steven D. Hanson, 1991. "Production, Hedging, and Speculative Decisions with Options and Futures Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(1), pages 66-74.
    6. Sergio H. Lence & Dermot J. Hayes & Yong Sakong, 1994. "Multiperiod Production with Forward and Option Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(2), pages 286-295.
    7. Tronstad, Russell, 1991. "The Effects of Firm Size and Production Cost Levels on Dynamically Optimal After-Tax Cotton Storage and Hedging Decisions," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 23(1), pages 165-179, July.
    8. Tronstad, Russell, "undated". "Optimal Cash Grain Sale, Storage, and Hedging Decisions for Grain Producers: A Stochastic Dynamic Programming Analysis," 1989 Annual Meeting, July 30-August 2, Baton Rouge, Louisiana 270518, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Vadhindran K. Rao, 2011. "Multiperiod Hedging using Futures: Mean Reversion and the Optimal Hedging Path," JRFM, MDPI, vol. 4(1), pages 1-29, December.
    10. Frank, Deon, 1992. "Agricultural Commodity Futures Markets In South Africa," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 31(4), December.
    11. Zhao, Jieyuan & Goodwin, Barry K., 2012. "Dynamic Cross-Hedge Ratios: An Application of Copula Models," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124610, Agricultural and Applied Economics Association.
    12. Anderson, Jock R., 2003. "Risk in rural development: challenges for managers and policy makers," Agricultural Systems, Elsevier, vol. 75(2-3), pages 161-197.
    13. Nyassoke Titi Gaston Clément & Sadefo Kamdem Jules & Fono Louis Aimé, 2022. "Dynamic optimal hedge ratio design when price and production are stochastic with jump," Annals of Finance, Springer, vol. 18(3), pages 419-428, September.
    14. Jing-Yi Lai, 2012. "An empirical study of the impact of skewness and kurtosis on hedging decisions," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1827-1837, December.

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