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.
PurposeThe purpose of this paper is to estimate crop yield densities considering time trends in the first three moments and spatially varying coefficients.Design/methodology/approachYield density parameters are assumed to be spatially correlated, through a Gaussian spatial process. This study spatially smooth multiple parameters using Bayesian Kriging.FindingsAssuming that county yields follow skew normal distributions, the location parameter increased faster in the eastern and northwestern counties of Iowa, while the scale increased faster in southern counties and the shape parameter increased more (implying less left skewness) in southwestern counties. Over time, the mean has increased sharply, while the variance and left skewness increased modestly.Originality/valueBayesian Kriging can smooth time-varying yield distributions, handle unbalanced panel data and provide estimates when data are missing. Most past models used a two-stage estimation procedure, while our procedure estimates parameters jointly.
We determine how monthly precipitation and temperature before and during planting impacts US corn and soybean prevented planting acres using county‐level data from 1996 to 2019. Corn and soybean prevented planting acres were responsive to precipitation and temperature. Precipitation from January through May impacted corn prevented planting acres, but precipitation in May and June impacted soybean prevented planting acres. Additionally, higher average temperature in April decreased corn and soybean prevented planting acres. We find the cooler and wetter April and May months will increase the prevented planting acres. Results provide insight into how weather can impact prevented planting acres and indemnities.
This article evaluates Agriculture Risk Coverage (ARC) and Revenue Protection (RP) used in conjunction as an optimal risk management strategy for representative producers in the Corn Belt and Mississippi Delta. Using a simulation procedure to produce representative farm revenues, we find it is optimal under expected utility for producers to enroll in RP, despite having RP through ARC. Results are robust across alternative sampling methods and regions. These findings imply that ARC is better suited as a complementary program, and that it is optimal for a producer to enroll in higher coverage levels than we currently observe.
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