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A spatial Bayesian approach to weather derivatives

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  • Nicholas D. Paulson
  • Chad E. Hart
  • Dermot J. Hayes

Abstract

Purpose - While the demand for weather‐based agricultural insurance in developed regions is limited, there exists significant potential for the use of weather indexes in developing areas. The purpose of this paper is to address the issue of historical data availability in designing actuarially sound weather‐based instruments. Design/methodology/approach - A Bayesian rainfall model utilizing spatial kriging and Markov chain Monte Carlo techniques is proposed to estimate rainfall histories from observed historical data. An example drought insurance policy is presented where the fair rates are calculated using Monte Carlo methods and a historical analysis is carried out to assess potential policy performance. Findings - The applicability of the estimation method is validated using a rich data set from Iowa. Results from the historical analysis indicate that the systemic nature of weather risk can vary greatly over time, even in the relatively homogenous region of Iowa. Originality/value - The paper shows that while the kriging method may be more complex than competing models, it also provides a richer set of results. Furthermore, while the application is specific to forage production in Iowa, the rainfall model could be generalized to other regions by incorporating additional climatic factors.

Suggested Citation

  • Nicholas D. Paulson & Chad E. Hart & Dermot J. Hayes, 2010. "A spatial Bayesian approach to weather derivatives," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 70(1), pages 79-96, May.
    • Handle: RePEc:eme:afrpps:v:70:y:2010:i:1:p:79-96
    DOI: 10.1108/00021461011042657
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    References listed on IDEAS

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    1. Chad E. Hart & Bruce A. Babcock & Dermot J. Hayes, 2001. "Livestock Revenue Insurance," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(6), pages 553-580, June.
    2. 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. 26(1), pages 1-14, July.
    3. Takeshi Sakurai & Thomas Reardon, 1997. "Potential Demand for Drought Insurance in Burkina Faso and Its Determinants," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(4), pages 1193-1207.
    4. Gautam, Madhur & Hazell, Peter & Alderman, Harold, 1994. "Rural demand for drought insurance," Policy Research Working Paper Series 1383, The World Bank.
    5. Nancy McCarthy, 2003. "Demand for rainfall-index based insurance: a case study from Morocco," EPTD discussion papers 106, International Food Policy Research Institute (IFPRI).
    6. Jerry R. Skees, 2008. "Challenges for use of index‐based weather insurance in lower income countries," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 68(1), pages 197-217, May.
    7. Jerry R. Skees & Barry J. Barnett, 1999. "Conceptual and Practical Considerations for Sharing Catastrophic/Systemic Risks," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 21(2), pages 424-441.
    8. John Duncan & Robert J. Myers, 2000. "Crop Insurance under Catastrophic Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(4), pages 842-855.
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    Cited by:

    1. Aditya Kusuma & Bethanna Jackson & Ilan Noy, 2018. "A viable and cost-effective weather index insurance for rice in Indonesia," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(2), pages 186-218, September.
    2. Aditya Kusuma & Bethanna Jackson & Ilan Noy, 2018. "A viable and cost-effective weather index insurance for rice in Indonesia," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(2), pages 186-218, September.
    3. Xiaofeng Cao & Ostap Okhrin & Martin Odening & Matthias Ritter, 2015. "Modelling spatio-temporal variability of temperature," Computational Statistics, Springer, vol. 30(3), pages 745-766, September.

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