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

Abstract

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.

A Portfolio Optimization Approach Using Combinatorics With a Genetic Algorithm for Developing a Reinsurance Model

“…Liability is a measure of the insurer's exposure to loss, and this calculation is largely dependent on crop yield. Extensive literature exists for restating crop yields for modeling purposes, in which scholars emphasize the importance of accounting for increasing trends in yields over time (Miranda and Glauber, ; Sherrick et al, ; Woodard, Paulson, and Vedenov, ). Increasing yield trends can largely be explained by advances in technology, biotechnology, and farming practices, in which empirical analysis generally supports capturing these agricultural effects using a deterministic trend over a stochastic trend (Coble et al, ; Harri et al, ; Zhu, Porth, and Tan, ).…”