Journal of Agricultural and Resource Economics
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The theory of the natural hedge states that agricultural yields and prices are inversely related. Actuarial rules for U.S. crop revenue insurance assume that dependence between yield and price is constant across all counties within a state and that dependence can be adequately described by the Gaussian copula. We use nonlinear measures of association and a selection of bivariate copulas to empirically characterize spatially-varying dependence between prices and yields and examine premium rate sensitivity for all corn producing counties in the United States. A simulation analysis across copula types and parameter values exposes hypothetical impacts of actuarial changes.
The objective of this study is to evaluate the risk associated with major agricultural commodity yields in the United States. We are particularly concerned with the nonstationary nature of the yield distribution, which arises primarily as a result of technological progress and changing environmental conditions over time. In contrast to common two-stage methods, we propose an alternative parametric model that allows the moments of yield distributions to change with time. Several model selection techniques suggest the proposed time-varying model outperforms more conventional models in terms of in-sample goodness-of-fit, out-of-sample predictive power, and the prediction accuracy of insurance premium rates.
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