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The Risk Analysis in the Agricultural Enterprises using Earnings at Risk Method

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  • Jindřich Špička
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    Abstract

    The paper examines the potential of stochastic simulation methods and Earnings at Risk method in risk analysis of farming business. The results revealed a different nature of yield and price risks in agriculture. The natural yields are low spatially correlated and the rate of yield risk depends on the climate and weather features, soil properties, technology of production and other predominantly natural variables. Estimates of yield probability distribution require the most individualized data. Price risk has higher spatial correlation. The case study illustrates that the Monte Carlo simulation and Earnings at Risk method are suitable tools for risk analysis in econometric models at the farm level as well as at the aggregate level.

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    Bibliographic Info

    Article provided by University of Economics, Prague in its journal Ekonomika a Management.

    Volume (Year): 2009 (2009)
    Issue (Month): 3 ()
    Pages:

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    Handle: RePEc:prg:jnleam:v:2009:y:2009:i:3:id:75

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    Postal: Redakce Ekonomika a management, Vysoká škola ekonomická v Praze, nám. W. Churchilla 4, 130 67 Praha 3
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    Related research

    Keywords: Zemědělství; Earnings at Risk; Monte Carlo; analýza rizika; stochastická simulace; Agriculture; risk analysis; stochastic simulation;

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    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Richard E. Just & Quinn Weninger, 1999. "Are Crop Yields Normally Distributed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 287-304.
    2. Coble, Keith H. & Heifner, Richard G. & Zuniga, Manuel, 2000. "Implications Of Crop Yield And Revenue Insurance For Producer Hedging," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(02), December.
    3. Weisensel, Ward P. & Schoney, Richard A., 1989. "An Analysis Of The Yield-Price Risk Associated With Specialty Crops," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 14(02), December.
    4. Ray, Daryll E. & Richardson, James W. & Torre Ugarte, Daniel de la & Tiller, Kelly, 1998. "Estimating Price Variability In Agriculture: Implications For Decision Makers," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 30(01), July.
    5. Octavio A. Ram�rez, 1997. "Estimation and Use of a Multivariate Parametric Model for Simulating Heteroskedastic, Correlated, Nonnormal Random Variables: The Case of Corn Belt Corn, Soybean, and Wheat Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 191-205.
    6. Goodwin, Barry K. & Roberts, Matthew C. & Coble, Keith H., 2000. "Measurement Of Price Risk In Revenue Insurance: Implications Of Distributional Assumptions," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(01), July.
    7. Michael Popp & Margot Rudstrom & Patrick Manning, 2005. "Spatial Yield Risk Across Region, Crop and Aggregation Method," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 53(2-3), pages 103-115, 06.
    8. Coble, Keith H. & Dismukes, Robert & Thomas, Sarah E., 2007. "Policy Implications of Crop Yield and Revenue Variability at Differing Levels of Disaggregation," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 9759, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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