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Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory

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  • Odening, Martin
  • Hinrichs, Jan

Abstract

The objective of this paper is to investigate the performance of different Value-at-Risk (VaR) models in the context of risk assessment in hog production. The paper starts with a description of traditional VaR models, i.e. Variance-Covariance-Method (VCM) and Historical Simulation (HS). We address two well known problems, namely the fat tailedness of return distributions and the time aggregation of VaR forecasts. Afterwards, Extreme-Value-Theory (EVT) is introduced in order to overcome these problems. The previously described methods are then used to calculate the VaR of hog production under German market conditions. It turns out that EVT, VCM, and HS lead to different VaR forecasts if the return distributions are fat tailed and if the forecast horizon is long. Finally, we discuss the strengths and weaknesses of these rather new risk management methods thereby trying to identify fields for potential applications in the agribusiness.

Suggested Citation

  • Odening, Martin & Hinrichs, Jan, 2003. "Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 0(Number 2), pages 1-11.
  • Handle: RePEc:ags:gjagec:98092
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    References listed on IDEAS

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    1. Seung-Ryong Yang & B. Wade Brorsen, 1992. "Nonlinear Dynamics of Daily Cash Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(3), pages 706-715.
    2. Bahrs, E., 2001. "Methoden des Rechnungswesens als Instrumente des Risikomanagements in der Landwirtschaft," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 0.
    3. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-927, July.
    4. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    5. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
    6. Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," Center for Financial Institutions Working Papers 98-10, Wharton School Center for Financial Institutions, University of Pennsylvania.
    7. Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania.
    8. repec:adr:anecst:y:2000:i:60:p:10 is not listed on IDEAS
    9. Jón Daníelsson & Casper G. de Vries, 1998. "Beyond the Sample: Extreme Quantile and Probability Estimation," Tinbergen Institute Discussion Papers 98-016/2, Tinbergen Institute.
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    Cited by:

    1. Musshoff, Oliver & Hirschauer, Norbert & Palmer, Ken, 2002. "Bounded Recursive Stochastic Simulation - A Simple and Efficient Method for Pricing Complex American Type Options," Working Paper Series 18823, Humboldt University Berlin, Department of Agricultural Economics.

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    Keywords

    Farm Management; Risk and Uncertainty;

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