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Coherent Quantitative Analysis of Risks in Agribusiness: Case of Ukraine


  • Tarasov, Arthur


Modern methods of quantitative risk analysis, specifically value-at-risk and expected shortfall approach, provide comprehensive and coherent risk evaluation throughout entire distribution of outcomes and can take agricultural business from the realm of uncertainty to specific, quantified risks. Monte Carlo simulation with autocorrelation of standard deviation shows the best results in risk modeling and is used for this research. The analysis showed that production risk is systemic within climatic regions of Ukraine with coefficients of correlation ranging from 0.25 to 0.85. Yield correlation among crops in several oblasts is low to negative, creating opportunities for diversification. However, positive price-yield correlation is dominant for agricultural products in Ukraine due to high dependency on global prices and a large share of export. It is hypothesized that price-yield correlation is directly proportional to the share of country’s international trade in that agricultural product.

Suggested Citation

  • Tarasov, Arthur, 2011. "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), December.
  • Handle: RePEc:ags:aolpei:120240

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    References listed on IDEAS

    1. Harwood, Joy L. & Heifner, Richard G. & Coble, Keith H. & Perry, Janet E. & Somwaru, Agapi, 1999. "Managing Risk in Farming: Concepts, Research, and Analysis," Agricultural Economics Reports 34081, United States Department of Agriculture, Economic Research Service.
    2. 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.
    3. Danielsson, Jon & Jorgensen, Bjorn N. & Sarma, Mandira & de Vries, Casper G., 2006. "Comparing downside risk measures for heavy tailed distributions," Economics Letters, Elsevier, vol. 92(2), pages 202-208, August.
    4. Ibrahim Onour, "undated". "Forecasting Volatility in Global Food Commodity Prices," API-Working Paper Series 1101, Arab Planning Institute - Kuwait, Information Center.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Yamai, Yasuhiro & Yoshiba, Toshinao, 2005. "Value-at-risk versus expected shortfall: A practical perspective," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 997-1015, April.
    7. Filip Iorgulescu, 2009. "Value at Risk: A Comparative Analysis," Advances in Economic and Financial Research - DOFIN Working Paper Series 25, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    8. Herwartz, Helmut, 2009. "Exact inference in diagnosing Value-at-Risk estimates -- A Monte Carlo device," Economics Letters, Elsevier, vol. 103(3), pages 160-162, June.
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