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This study assesses the performance of several alternative methods for modeling dependence between random variables in the context of pricing an agricultural insurance contract with multiple underlying risk exposures. Simulation methods are used to estimate the sampling distribution of the insurance rates generated under alternative methods. The results indicate significant variability in performance across methods, and contribute to the risk analysis and insurance literatures by quantitatively assessing out-of-sample efficiency and bias trade-off among competing methods for modeling dependence in limited data scenarios.

Estimation efficiency in the modeling of dependence structures: an application of alternative copulas to insurance rating