Predicting Multivariate Insurance Loss Payments Under the Bayesian Copula Framework

Abstract
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.

Keywords: Bayesian; Copula; Generalized linear model; Multivariate; Nonlinear model; Penalized splines

Volume
Vol. 80, Issue 4
Page
1-32
Year
2013
Categories
Financial and Statistical Methods
Statistical Models and Methods
Bayesian Methods
Financial and Statistical Methods
Simulation
Copulas/Multi-Variate Distributions
Financial and Statistical Methods
Statistical Models and Methods
Generalized Linear Modeling
Publications
Journal of Risk and Insurance, The
Prizes
American Risk and Insurance Association Prize
Authors
Author #6183
Author #6184

Keep up with the latest CAS news