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

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Author Info

  • 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.

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Bibliographic Info

Paper provided by Humboldt University Berlin, Department Agricultural Economics in its series Working Paper Series with number 18826.

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Date of creation: 2002
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Handle: RePEc:ags:huiawp:18826

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Related research

Keywords: Risk and Uncertainty;

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References

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  1. Drost, F.C. & Nijman, T.E., 1990. "Temporal aggregation of GARCH processes," Discussion Paper 1990-66, Tilburg University, Center for Economic Research.
  2. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-.
  3. Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-081, New York University, Leonard N. Stern School of Business-.
  4. Jon DANIELSSON & Casper G. DE VRIES, 2000. "Value-at-Risk and Extreme Returns," Annales d'Economie et de Statistique, ENSAE, issue 60, pages 239-270.
  5. Jón Daníelsson & Casper G. de Vries, 1998. "Value-at-Risk and Extreme Returns," Tinbergen Institute Discussion Papers 98-017/2, Tinbergen Institute.
  6. 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.
  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. Jon Danielsson & Casper G. de Vries, 1998. "Beyond the Sample: Extreme Quantile and Probability Estimation," FMG Discussion Papers dp298, Financial Markets Group.
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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 Agricultural Economics.

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