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Nonparametric estimation of market risk: an application to agricultural commodity futures

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  • Abdoul G. Sam
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    Abstract

    Purpose – While the extant literature is replete with theoretical and empirical studies of value at risk (VaR) methods, only a few papers have applied the concept of VaR to quantify market risk in the context of agricultural finance. Furthermore, papers that have done so have largely relied on parametric methods to recover estimates of the VaR. The purpose of this paper is to assess extreme market risk on investment in three actively traded agricultural commodity futures. Design/methodology/approach – A nonparametric Kernel method was implemented which accommodates fat tails and asymmetry of the portfolio return density as well as serial correlation of the data, to estimate market risk for investments in three actively traded agricultural futures contracts: corn, soybeans, and wheat. As a futures contract is a zero-sum game, the VaR for both short and long sides of the market was computed. Findings – It was found that wheat futures are riskier than either corn or soybeans futures over both periods considered in the study (2000-2008 and 2006-2008) and that all three commodities have experienced a sharp increase in market risk over the 2006-2008 period, with VaR estimates 10-43 percent higher than the long-run estimates. Research limitations/implications – Research is based on cross-sectional data and does not allow for dynamic assessment of expenditure elasticities. Originality/value – This paper differs methodologically from previous applications of VaR in agricultural finance in that a nonparametric Kernel estimator was implemented which is exempt of misspecification risk, in the context of risk management of investment in agricultural futures contracts. The application is particularly relevant to grain elevator businesses which purchase grain from farmers on a forward contract basis and then turn to the futures markets to insure against falling prices.

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    Bibliographic Info

    Article provided by Emerald Group Publishing in its journal Agricultural Finance Review.

    Volume (Year): 70 (2010)
    Issue (Month): 2 (August)
    Pages: 285-297

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    Handle: RePEc:eme:afrpps:v:70:y:2010:i:2:p:285-297

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    Related research

    Keywords: Agriculture; Contracts; Futures markets; United States of America; Value analysis;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Yacine Ait-Sahalia, 1995. "Testing Continuous-Time Models of the Spot Interest Rate," NBER Working Papers 5346, National Bureau of Economic Research, Inc.
    2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, April.
    3. Gouriéroux, Christian & Laurent, J.P. & Scaillet, Olivier, 1999. "Sensitivity Analysis of Values at Risk," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2000002, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES), revised 00 Jan 2000.
    4. Brooks, C. & Clare, A.D. & Dalle Molle, J.W. & Persand, G., 2005. "A comparison of extreme value theory approaches for determining value at risk," Journal of Empirical Finance, Elsevier, vol. 12(2), pages 339-352, March.
    5. William W. Wilson & William E. Nganje & Cullen R. Hawes, 2007. "Value-at-Risk in Bakery Procurement," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 29(3), pages 581-595.
    6. Song Xi Chen, 2005. "Nonparametric Inference of Value-at-Risk for Dependent Financial Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(2), pages 227-255.
    7. Sam, Abdoul G. & Jiang, George J., 2009. "Nonparametric Estimation of the Short Rate Diffusion Process from a Panel of Yields," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(05), pages 1197-1230, October.
    8. Christian Gourieroux & Jean-Paul Laurent & Olivier Scaillet, 2000. "Sensitivity Analysis of Values at Risk," Working Papers 2000-05, Centre de Recherche en Economie et Statistique.
    9. Chu-Hsiung Lin & Shan-Shan Shen, 2006. "Can the student-t distribution provide accurate value at risk?," Journal of Risk Finance, Emerald Group Publishing, vol. 7(3), pages 292-300, May.
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