Copula Averaging for Tail Dependence in Insurance Claims Data

TL;DR

This paper introduces a Bayesian model averaging approach to estimate tail dependence in insurance claims data, providing a unified measure of extreme risk dependence across multiple copula models.

Contribution

It proposes a novel copula averaging method for more reliable tail dependence estimation in insurance risk analysis.

Findings

Effective in simulated data scenarios

Applied successfully to real insurance loss data

Improves robustness of tail dependence estimates

Abstract

Analysing dependent risks is an important task for insurance companies. A dependency is reflected in the fact that information about one random variable provides information about the likely distribution of values of another random variable. Insurance companies in particular must investigate such dependencies between different lines of business and the effects that an extreme loss event, such as an earthquake or hurricane, has across multiple lines of business simultaneously. Copulas provide a popular model-based approach to analysing the dependency between risks, and the coefficient of tail dependence is a measure of dependence for extreme losses. Besides commonly used empirical estimators for estimating the tail dependence coefficient, copula fitting can lead to estimation of such coefficients directly or can verify their existence. Generally, a range of copula models is available to fit a data set well, leading to multiple different tail dependence results; a method based on Bayesian model averaging is designed to obtain a unified estimate of tail dependence. In this article, this model-based coefficient estimation method is illustrated through a variety of copula fitting approaches and results are presented for several simulated data sets and also a real general insurance loss data set.