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On the systemic nature of weather risk

Author

Listed:
  • Wei Xu
  • Guenther Filler
  • Martin Odening
  • Ostap Okhrin

Abstract

Purpose - The purpose of this paper is to assess the losses of weather-related insurance at different regional levels. The possibility of spatial diversification of insurance is explored by estimating the joint occurrence on unfavorable weather conditions in different locations, looking particularly at the tail behavior of the loss distribution. Design/methodology/approach - Joint weather-related losses are estimated using copulas. Copulas avoid the direct estimation of multivariate distributions but allow for much greater flexibility in modeling the dependence structure of weather risks compared with simple correlation coefficients. Findings - Results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a large regional scale. Thus the possibility to reduce risk exposure by increasing the trading area of insurance is limited. Research limitations/implications - The empirical findings are limited by a rather weak database. In that case the estimation of high-dimensional copulas leads to large estimation errors. Practical implications - The paper includes implications for the quantification of systemic weather risk which is important for the rate making of crop insurance and reinsurance. Originality/value - This paper's results highlight how important the choice of the statistical approach is when modeling the dependence structure of weather risks.

Suggested Citation

  • 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.
  • Handle: RePEc:eme:afrpps:v:70:y:2010:i:2:p:267-284
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    References listed on IDEAS

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    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. Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
    3. Martin, Steven W. & Barnett, Barry J. & Coble, Keith H., 2001. "Developing And Pricing Precipitation Insurance," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 0(Number 1), pages 1-14, July.
    4. H. Holly Wang & Hao Zhang, 2003. "On the Possibility of a Private Crop Insurance Market: A Spatial Statistics Approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 70(1), pages 111-124.
    5. Xu, Wei & Odening, Martin & Musshoff, Oliver, 2008. "Optimal Design of Weather Bonds," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6781, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. 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.
    7. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
    8. Barry K. Goodwin, 2001. "Problems with Market Insurance in Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 643-649.
    9. Enzo Giacomini & Wolfgang Härdle, 2005. "Value-at-Risk Calculations with Time Varying Copulae," SFB 649 Discussion Papers SFB649DP2005-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Martin Odening & Oliver Musshoff & Wei Xu, 2007. "Analysis of rainfall derivatives using daily precipitation models: opportunities and pitfalls," Agricultural Finance Review, Emerald Group Publishing, vol. 67(1), pages 135-156, May.
    11. Markus Junker & Angelika May, 2005. "Measurement of aggregate risk with copulas," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 428-454, December.
    12. World Bank, 2005. "Managing Agricultural Production Risk : Innovations in Developing Countries," World Bank Other Operational Studies 14434, The World Bank.
    13. Mario J. Miranda & Joseph W. Glauber, 1997. "Systemic Risk, Reinsurance, and the Failure of Crop Insurance Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 206-215.
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    Citations

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    Cited by:

    1. Roland Strausz, 2009. "The Political Economy of Regulatory Risk," SFB 649 Discussion Papers SFB649DP2009-040, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Buchholz, Matthias & Musshoff, Oliver, 2014. "The role of weather derivatives and portfolio effects in agricultural water management," Agricultural Water Management, Elsevier, vol. 146(C), pages 34-44.
    3. Schulte-Geers, Matthias & Berg, Ernst, 2011. "Modelling farm production risk with copulae instead of correlations," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115996, European Association of Agricultural Economists.
    4. Zhiwei Shen & Martin Odening, 2013. "Coping with systemic risk in index-based crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 1-13, January.
    5. Michał Grajek & Lars-Hendrik Röller, 2012. "Regulation and Investment in Network Industries: Evidence from European Telecoms," Journal of Law and Economics, University of Chicago Press, vol. 55(1), pages 189-216.
    6. Barbara Choroś & Wolfgang Härdle & Ostap Okhrin, 2009. "CDO and HAC," SFB 649 Discussion Papers SFB649DP2009-038, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Chung, Wonho, 2013. "Reducing the Social Cost of Federal Crop Insurance: A Role for US Government Hedging with Weather Derivatives," Journal of Rural Development/Nongchon-Gyeongje, Korea Rural Economic Institute, vol. 0(Issue 2), pages 1-26, August.
    8. Bokusheva, Raushan, 2010. "Measuring the dependence structure between yield and weather variables," MPRA Paper 22786, University Library of Munich, Germany.
    9. 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, 2014, Louisville, KY 184241, Agricultural and Applied Economics Association.
    10. M. Ritter & O. Mußhoff & M. Odening, 2014. "Minimizing Geographical Basis Risk of Weather Derivatives Using A Multi-Site Rainfall Model," Computational Economics, Springer;Society for Computational Economics, vol. 44(1), pages 67-86, June.
    11. Ostap Okhrin & Martin Odening & Wei Xu, 2013. "Systemic Weather Risk and Crop Insurance: The Case of China," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(2), pages 351-372, June.
    12. 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.
    13. Maria Grith & Wolfgang Härdle & Juhyun Park, 2009. "Shape invariant modelling pricing kernels and risk aversion," SFB 649 Discussion Papers SFB649DP2009-041, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Ostap Okhrin, 2010. "Fitting high-dimensional Copulae to Data," SFB 649 Discussion Papers SFB649DP2010-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Lysa Porth & Milton Boyd & Jeffrey Pai, 2016. "Reducing Risk Through Pooling and Selective Reinsurance Using Simulated Annealing: An Example from Crop Insurance," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 41(2), pages 163-191, September.

    More about this item

    Keywords

    Agriculture; Crops; Insurance; Financial risk; Multivariate analysis;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Q19 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Other

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