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Extreme Measures of Agricultural Financial Risk

Author

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  • Wyn Morgan
  • John Cotter
  • Kevin Dowd

Abstract

Risk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value at Risk (VaR), Expected Shortfall (ES) and Spectral Risk Measures (SRMs). We use Extreme Value Theory (EVT) to model the tail returns and present results for these three different risk measures using agricultural futures market data. We compare the estimated risk measures in terms of their size and precision, and find that they are all considerably higher than normal estimates; they are also quite uncertain, and become more uncertain as the risks involved become more extreme.
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Suggested Citation

  • 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.
    • Handle: RePEc:bla:jageco:v:63:y:2012:i:1:p:65-82
    DOI: j.1477-9552.2011.00322.x
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    File URL: http://hdl.handle.net/10.1111/j.1477-9552.2011.00322.x
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    Cited by:

    1. Athanasios Triantafyllou & George Dotsis & Alexandros Sarris, 2020. "Assessing the Vulnerability to Price Spikes in Agricultural Commodity Markets," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 631-651, September.
    2. Just, Małgorzata & Śmiglak-Krajewska, Magdalena, 2015. "Extreme Price Risk on the Market of Rapeseeds and Processed Rapeseed Products in Poland," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2015(5), October.
    3. Tarasov, Arthur, . "Coherent Quantitative Analysis of Risks in Agribusiness: Case of Ukraine," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 3(4), pages 1-7.
    4. Dejan Živkov & Marijana Joksimović & Suzana Balaban, 2021. "Measuring parametric and semiparametric downside risks of selected agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(8), pages 305-315.
    5. Just, Małgorzata & Śmiglak-Krajewska, Magdalena, 2015. "Extreme Price Risk on the Market of Soybean Meal," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 15(30), pages 1-9, December.
    6. Motengwe, Chris & Alagidede, Paul, 2016. "Maturity Effects in Futures Contracts on the SAFEX Market," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 55(4), December.
    7. Husemann, Christoph & Novković, Nebojša, 2014. "Farm Management Information Systems: A Case Study On A German Multifunctional Farm," Economics of Agriculture, Institute of Agricultural Economics, vol. 61(2), pages 1-13, June.
    8. Małgorzata Just & Aleksandra Łuczak, 2020. "Assessment of Conditional Dependence Structures in Commodity Futures Markets Using Copula-GARCH Models and Fuzzy Clustering Methods," Sustainability, MDPI, vol. 12(6), pages 1-22, March.

    More about this item

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G19 - Financial Economics - - General Financial Markets - - - Other
    • N52 - Economic History - - Agriculture, Natural Resources, Environment and Extractive Industries - - - U.S.; Canada: 1913-

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