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Market risk and the cattle feeding margin: An application of Value-at-Risk

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  • Mark R. Manfredo

    (Morrison School of Agribusiness and Resource Management,, Arizona State University East, Mail Code 0180, 7001 E. Williams Field Rd., Bldg. 20, Mesa, AZ 85212. E-mail: manfredo@asu.edu)

  • Raymond M. Leuthold

    (Office for Futures and Options Research, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign,, 305 Mumford Hall, 1301 West Gregory Drive, Urbana, IL 61801., E-mail: rmleuth@uiuc.edu)

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    Abstract

    Value-at-Risk, known as VaR, gives a prediction with a certain level of confidence of potential portfolio losses that may be encountered over a specified time period due to adverse price movements in the portfolio's assets. For example, a VaR of 1 million dollars at the 95% level of confidence implies that overall portfolio losses should not exceed 1 million dollars more than 5% of the time over a given holding period. This research examines the effectiveness of VaR measures, developed using alternative estimation techniques, in predicting large losses in the cattle-feeding margin. Results show that several estimation techniques, both parametric and nonparametric, provide well-calibrated estimates of VaR such that violations (losses exceeding the VaR estimate) are commensurate with the desired level of confidence. In particular, estimates developed using the RiskMetrics TM method appear robust for instruments that have linear payoff structures such as cash commodity prices. © 2001 John Wiley & Sons, Inc.

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    File URL: http://hdl.handle.net/10.1002/agr.1020
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Agribusiness.

    Volume (Year): 17 (2001)
    Issue (Month): 3 ()
    Pages: 333-353

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    Handle: RePEc:wly:agribz:v:17:y:2001:i:3:p:333-353

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    Web page: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1520-6297

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    1. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    2. 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.
    3. Anil K. Bera & Philip Garcia & Jae-Sun Roh, 1997. "Estimation of Time-Varying Hedge Ratios for Corn and Soybeans: BGARCH and Random Coefficient Approaches," Finance 9712007, EconWPA.
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    Cited by:
    1. Al Janabi, Mazin A.M., 2012. "Optimal commodity asset allocation with a coherent market risk modeling," Review of Financial Economics, Elsevier, vol. 21(3), pages 131-140.
    2. Mark R. Manfredo & Dwight R. Sanders, 2004. "The forecasting performance of implied volatility from live cattle options contracts: Implications for agribusiness risk management," Agribusiness, John Wiley & Sons, Ltd., vol. 20(2), pages 217-230.
    3. Wyn Morgan & John Cotter & Kevin Dowd, 2012. "Extreme Measures of Agricultural Financial Risk," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(1), pages 65-82, 02.
    4. Larsen, Ryan A. & Leatham, David J. & Mjelde, James W. & Wolfley, Jared L., 2008. "Geographical Diversification: An Application of Copula Based CVaR," Proceedings:2008 Agricultural and Rural Finance Markets in Transition, September 25-26, 2008, Kansas City, Missouri 119533, Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition.
    5. Chen, Songjiao & Wilson, William W. & Larsen, Ryan A. & Dahl, Bruce L., 2013. "Investing in Agriculture as an Asset Class," Agribusiness & Applied Economics Report 147053, North Dakota State University, Department of Agribusiness and Applied Economics.

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