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Agricultural Applications of Value-at-Risk Analysis: A Perspective

  • Mark R. Manfredo

    (University of Illinois at Urbana-Champaign)

  • Raymond M. Leuthold
Registered author(s):

    Value-at-Risk (VaR) determines the probability of a portfolio of assets losing a certain amount in a given time period due to adverse market conditions with a particular level of confidence. Value-at-Risk has received considerable attention from financial economists and financial practitioners for its use in risk reporting, in particular the risks of derivatives. This paper provides a "state-of-the-art" review of VaR estimation techniques and empirical findings found in the finance literature. The ability of VaR estimates to represent large losses associated with tail events varies among procedure, confidence level, and data used. To date, there is no consensus to the most appropriate estimation technique. Potential applications of Value-at-Risk are suggested in the context of agricultural risk management. In the wake of the Hedge-to-Arrive crisis, the lifting of agricultural trade options by the CFTC, and the decreased government participation, VaR seems to have a place in the agricultural risk manager's toolkit.

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    File URL: http://128.118.178.162/eps/fin/papers/9805/9805002.pdf
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    Paper provided by EconWPA in its series Finance with number 9805002.

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    Length: 14 pages
    Date of creation: 04 May 1998
    Date of revision:
    Handle: RePEc:wpa:wuwpfi:9805002
    Note: Type of Document - pdf; prepared on pc; to print on HP Laserjet; pages: 14. Office for Futures and Options Research (OFOR) at the University of Illinois at Urbana-Champaign. Working Paper 98-04. For a complete list of OFOR working papers see
    Contact details of provider: Web page: http://128.118.178.162

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    1. Peter F. Christoffersen & Francis X. Diebold, 1997. "How Relevant is Volatility Forecasting for Financial Risk Management?," Center for Financial Institutions Working Papers 97-45, Wharton School Center for Financial Institutions, University of Pennsylvania.
    2. Patricia Jackson & David Maude & William Perraudin, 1998. "Bank Capital and Value at Risk," Bank of England working papers 79, Bank of England.
    3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    4. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, issue Apr, pages 39-69.
    5. Subu Venkataraman, 1997. "Value at risk for a mixture of normal distributions: the use of quasi- Bayesian estimation techniques," Economic Perspectives, Federal Reserve Bank of Chicago, issue Mar, pages 2-13.
    6. Thomas J. Linsmeier & Neil D. Pearson, 1996. "Risk Measurement: An Introduction to Value at Risk," Finance 9609004, EconWPA.
    7. Linsmeier, Thomas J. & Pearson, Neil D., 1996. "Risk measurement: an introduction to value at risk," ACE Reports 14796, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    8. Seung‐Ryong Yang & B. Wade Brorsen, 1993. "Nonlinear dynamics of daily futures prices: Conditional heteroskedasticity or chaos?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(2), pages 175-191, 04.
    9. 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.
    10. Gregory P. Hopper, 1996. "Value at risk: a new methodology for measuring portfolio risk," Business Review, Federal Reserve Bank of Philadelphia, issue Jul, pages 19-31.
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