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Measuring dependence in joint distributions of yield and weather variables


  • Raushan Bokusheva


Purpose - The design and pricing of weather-based insurance instruments is strongly based on an implicit assumption that the dependence structure between crop yields and weather variables remains unchanged over time. The purpose of this paper is to verify this critical assumption by employing historical time series of weather and farm yields from a semi-arid region. Design/methodology/approach - The analysis employs two different approaches to measure dependence in multivariate distributions – the regression analysis and copula approach. The estimations are done by employing Bayesian hierarchical model. Findings - The paper reveals statistically significant temporal changes in the joint distribution of weather variables and wheat yields for grain-producing farms in Kazakhstan over the period from 1961 to 2003. Research limitations/implications - By questioning its basic assumption the paper draws attention to serious limitations in the current methodology of the weather-based insurance design. Practical implications - The empirical results obtained indicate that the relationship between weather and crop yields is not fixed and can change over time. Accordingly, greater effort is required to capture potential temporal changes in the weather-yield-relationship and to consider them while developing and rating weather-based insurance instruments. Originality/value - The estimation of selected copula and regression models has been done by employing Bayesian hierarchical models.

Suggested Citation

  • Raushan Bokusheva, 2011. "Measuring dependence in joint distributions of yield and weather variables," Agricultural Finance Review, Emerald Group Publishing, vol. 71(1), pages 120-141, May.
  • Handle: RePEc:eme:afrpps:v:71:y:2011:i:1:p:120-141

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    References listed on IDEAS

    1. Vedenov, Dmitry V., 2008. "Application of Copulas to Estimation of Joint Crop Yield Distributions," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6264, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Oliver Musshoff & Martin Odening & Wei Xu, 2009. "Management of climate risks in agriculture-will weather derivatives permeate?," Applied Economics, Taylor & Francis Journals, vol. 43(9), pages 1067-1077.
    3. Jerry R. Skees & J. Roy Black & Barry J. Barnett, 1997. "Designing and Rating an Area Yield Crop Insurance Contract," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 430-438.
    4. Skees, Jerry*Gober, Stephanie*Varangis, Panos*Le, 2001. "Developing rainfall-based index insurance in Morocco," Policy Research Working Paper Series 2577, The World Bank.
    5. Trivedi, Pravin K. & Zimmer, David M., 2007. "Copula Modeling: An Introduction for Practitioners," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(1), pages 1-111, April.
    6. Wei Xu & Guenther Filler & Martin Odening & Ostap Okhrin, 2010. "On the systemic nature of weather risk," Agricultural Finance Review, Emerald Group Publishing, vol. 70(2), pages 267-284, August.
    7. Oliver Musshoff, 2008. "Indifference Pricing of Weather Derivatives," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 979-993.
    8. Wolfgang Härdle & Ostap Okhrin & Yarema Okhrin, 2008. "Modeling Dependencies in Finance using Copulae," SFB 649 Discussion Papers SFB649DP2008-043, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Skees, Jerry & Hazell, P. B. R. & Miranda, Mario, 1999. "New approaches to crop yield insurance in developing countries:," EPTD discussion papers 55, International Food Policy Research Institute (IFPRI).
    10. Fisher N. I. & Switzer P., 2001. "Graphical Assessment of Dependence: Is a Picture Worth 100 Tests?," The American Statistician, American Statistical Association, vol. 55, pages 233-239, August.
    11. Barry J. Barnett & Olivier Mahul, 2007. "Weather Index Insurance for Agriculture and Rural Areas in Lower-Income Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(5), pages 1241-1247.
    12. Bokusheva, Raushan & Breustedt, Gunnar, 2008. "Ex Ante Evaluation Of Index-Based Crop Insurance Effectiveness," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 43957, European Association of Agricultural Economists.
    13. Vedenov, Dmitry V. & Barnett, Barry J., 2004. "Efficiency of Weather Derivatives as Primary Crop Insurance Instruments," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(03), December.
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    Cited by:

    1. Zhiwei Shen & Martin Odening & Ostap Okhrin, 2016. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 43(2), pages 237-269.
    2. Feng, Xiaoguang & Hayes, Dermot, 2014. "Is Government Involvement Really Necessary: Implications for Systemic Risk and Crop Reinsurance Contracts," 2014 AAEA: Crop Insurance and the 2014 Farm Bill Symposium: Implementing Change in U.S. Agricultural Policy, October 8-9, Louisville, KY 184241, Agricultural and Applied Economics Association.
    3. Bokusheva, Raushan & Conradt, Sarah, 2012. "Catastrophic crop insurance effectiveness: does it make a difference how yield losses are conditioned?," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122443, European Association of Agricultural Economists.
    4. Bokusheva, Raushan, 2014. "Improving the Effectiveness of Weather-based Insurance: An Application of Copula Approach," MPRA Paper 62339, University Library of Munich, Germany.


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