IDEAS home Printed from https://ideas.repec.org/a/wly/agribz/v17y2001i3p333-353.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/agr.1020
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/agr.1020?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark R. Manfredo. & Raymond M. Leuthold, 1999. "Market Risk Measurement and the Cattle Feeding Margin: An Application of Value-at-Risk," Finance 9908002, University Library of Munich, Germany.
    2. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    3. Bessler, Wolfgang & Leonhardt, Alexander & Wolff, Dominik, 2016. "Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 239-256.
    4. Jian Yang & Titus Awokuse, 2003. "Asset storability and hedging effectiveness in commodity futures markets," Applied Economics Letters, Taylor & Francis Journals, vol. 10(8), pages 487-491.
    5. Y. K. Tse & Albert K. C. Tsui, 2000. "A Multivariate GARCH Model with Time-Varying correlations," Econometrics 0004010, University Library of Munich, Germany.
    6. Chang, Chia-Lin & McAleer, Michael & Wang, Yu-Ann, 2018. "Modelling volatility spillovers for bio-ethanol, sugarcane and corn spot and futures prices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1002-1018.
    7. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    8. Aglasan, Serkan & Wu, Shenan & Goodwin, Barry K., 2021. "Cross-hedging with Agricultural Commodities: A Copula-GARCH Approach," 2021 Annual Meeting, August 1-3, Austin, Texas 313960, Agricultural and Applied Economics Association.
    9. Vasiliki D. Skintzi & Spyros Xanthopoulos-Sisinis, 2007. "Evaluation of correlation forecasting models for risk management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 497-526.
    10. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    11. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    12. Sharma, Udayan & Karmakar, Madhusudan, 2023. "Measuring minimum variance hedging effectiveness: Traditional vs. sophisticated models," International Review of Financial Analysis, Elsevier, vol. 87(C).
    13. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    14. Algieri, Bernardina, 2014. "The influence of biofuels, economic and financial factors on daily returns of commodity futures prices," Energy Policy, Elsevier, vol. 69(C), pages 227-247.
    15. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    16. Tejeda, Hernan A. & Goodwin, Barry K. & Pelletier, Denis, 2009. "A State Dependent Regime Switching Model of Dynamic Correlations," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49370, Agricultural and Applied Economics Association.
    17. Hsiang-Tai Lee & Jonathan Yoder, 2007. "A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios," Applied Economics, Taylor & Francis Journals, vol. 39(10), pages 1253-1265.
    18. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
    19. Halkos, George & Tsirivis, Apostolos, 2019. "Using Value-at-Risk for effective energy portfolio risk management," MPRA Paper 91674, University Library of Munich, Germany.
    20. Castagneto-Gissey, G. & Nivorozhkin, E., 2016. "No contagion from Russia toward global equity markets after the 2014 international sanctions," Economic Analysis and Policy, Elsevier, vol. 52(C), pages 79-98.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:agribz:v:17:y:2001:i:3:p:333-353. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1520-6297 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.