Predicting Multivariate Insurance Loss Payments Under the Bayesian Copula Framework

The literature of estimating the outstanding liability for insurance companies has undergone rapid and profound changes in the past three decades, most recently focusing on Bayesian stochastic modeling and multivariate losses. In this paper we introduce a novel Bayesian multivariate model based on the use of parametric copula to model dependencies between various lines of insurance claims. We derive a full Bayesian stochastic simulation algorithm that can estimate parameters in this class of models. We provide an extensive discussion of this modeling framework, and give a sequence of examples that deal with a wide range of topics encountered in the multivariate loss prediction settings.