Journal
AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS
Volume 101, Issue 1, Pages 330-351Publisher
WILEY
DOI: 10.1093/ajae/aay045
Keywords
Area yield insurance rating; Bayesian Kriging; Bayesian spatial smoothing; extreme value theory; Gaussian spatial process; spatial econometrics
Categories
Funding
- A.J. & Susan Jacques Chair
- USDA National Institute of Food and Agriculture, Hatch Project [OKL02939]
- Oklahoma Agricultural Experiment Station, Hatch Project [OKL02939]
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Rating insurance policies depends on the probability of events in the tail of a distribution. A method to measure such tail-related risk based on Extreme Value Theory could potentially improve insurance rating. It is also widely agreed that there is a spatial structure to crop yield distributions. Considering the spatial structure may provide more precisely rated policies. In this context, this research provides two contributions in rating area yield crop insurance. One is to provide a method that fits the tail of crop yield distributions using the Generalized Pareto Distribution (GPD), a member of the family of extreme value distributions that models only the tail of the distribution. The second is to estimate parameters of the distribution using a Bayesian Kriging approach that provides spatial smoothing of GPD parameters. The proposed model provides estimates of the spatial structure across regions such as the maximum distance of the spatial effect. Based on an out-of-sample performance game between a private insurance company and the federal agency the proposed model provides considerable improvement, particularly when rating deeper tail probability.
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