A spatial Bayesian approach to weather derivatives
AbstractPurpose – 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Emerald Group Publishing in its journal Agricultural Finance Review.
Volume (Year): 70 (2010)
Issue (Month): 1 (May)
Contact details of provider:
Web page: http://www.emeraldinsight.com
Postal: Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK
Other versions of this item:
- Paulson, Nicholas D. & Hart, Chad E. & Hayes, Dermot J., 2010. "A Spatial Bayesian Approach to Weather Derivatives," Staff General Research Papers 31339, Iowa State University, Department of Economics.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Chad E. Hart & Bruce A. Babcock & Dermot J. Hayes, 2001.
"Livestock Revenue Insurance,"
Center for Agricultural and Rural Development (CARD) Publications
99-wp224, Center for Agricultural and Rural Development (CARD) at Iowa State University.
- Jerry R. Skees, 2008. "Challenges for use of index-based weather insurance in lower income countries," Agricultural Finance Review, Emerald Group Publishing, vol. 68(1), pages 197-217, September.
- Nancy McCarthy, 2003. "Demand for rainfall-index based insurance: a case study from Morocco," EPTD discussion papers 106, International Food Policy Research Institute (IFPRI).
- 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.
- Gautam, Madhur & Hazell, Peter & Alderman, Harold, 1994. "Rural demand for drought insurance," Policy Research Working Paper Series 1383, The World Bank.
- 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(01), July.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Louise Lister).
If references are entirely missing, you can add them using this form.