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

Author

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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)

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.

Suggested Citation

  • Mark R. Manfredo & Raymond M. Leuthold, 2001. "Market risk and the cattle feeding margin: An application of Value-at-Risk," Agribusiness, John Wiley & Sons, Ltd., vol. 17(3), pages 333-353.
    • Handle: RePEc:wly:agribz:v:17:y:2001:i:3:p:333-353
    DOI: 10.1002/agr.1020
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    References listed on IDEAS

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    1. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Apr), pages 39-69.
    2. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    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, University Library of Munich, Germany.
    4. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
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    Cited by:

    1. Songjiao Chen & William Wilson & Ryan Larsen & Bruce Dahl, 2016. "Risk Management for Grain Processors and “Copulas”," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(2), pages 365-382, June.
    2. 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. 52(02), pages 1-11.
    3. William E. Nganje & Linda D. Burbidge & Elisha K. Denkyirah & Elvis M. Ndembe, 2021. "Predicting Food-Safety Risk and Determining Cost-Effective Risk-Reduction Strategies," JRFM, MDPI, vol. 14(9), pages 1-18, September.
    4. Songjiao Chen & William W. Wilson & Ryan Larsen & Bruce Dahl, 2015. "Investing in Agriculture as an Asset Class," Agribusiness, John Wiley & Sons, Ltd., vol. 31(3), pages 353-371, June.
    5. Larsen, Ryan A. & Leatham, David J. & Mjelde, James W. & Wolfley, Jared L., 2008. "Geographical Diversification: An Application of Copula Based CVaR," 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.
    6. 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.
    7. 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. 37.
    8. 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.
    9. Martin ZIEGELBÄCK & Gregor KASTNER, 2013. "Arbitrage hedging in markets for the US lean hogs and the EU live pigs," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(11), pages 505-511.
    10. 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, February.
    11. Glynn Tonsor & Ted Schroeder, 2011. "Multivariate forecasting of a commodity portfolio: application to cattle feeding margins and risk," Applied Economics, Taylor & Francis Journals, vol. 43(11), pages 1329-1339.
    12. Larsen, Ryan A. & Vedenov, Dmitry V. & Leatham, David J., 2009. "Enterprise-level risk assessment of geographically diversified commercial farms: a copula approach," 2009 Annual Meeting, January 31-February 3, 2009, Atlanta, Georgia 46763, Southern Agricultural Economics Association.

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