On a Multi-Year Microlevel Collective Risk Model

TL;DR

This paper develops a multi-year microlevel collective risk model using elliptical copulas to capture complex dependencies between claim frequencies and severities over multiple years, enhancing insurance risk assessment.

Contribution

It extends existing one-year models to a multi-year framework, incorporating dependence among frequencies and severities across years with a novel copula-based approach.

Findings

Model captures various dependence structures effectively.

Calibration on real data shows strong evidence of dependencies.

Enhanced risk modeling for multi-year insurance portfolios.

Abstract

For a typical insurance portfolio, the claims process for a short period, typically one year, is characterized by observing frequency of claims together with the associated claims severities. The collective risk model describes this portfolio as a random sum of the aggregation of the claim amounts. In the classical framework, for simplicity, the claim frequency and claim severities are assumed to be mutually independent. However, there is a growing interest in relaxing this independence assumption which is more realistic and useful for the practical insurance ratemaking. While the common thread has been capturing the dependence between frequency and aggregate severity within a single period, the work of Oh et al. (2020a) provides an interesting extension to the addition of capturing dependence among individual severities. In this paper, we extend these works within a framework where we have a portfolio of microlevel frequencies and severities for multiple years. This allows us to develop a factor copula model framework that captures various types of dependence between claim frequencies and claim severities over multiple years. It is therefore a clear extension of earlier works on one-year dependent frequency-severity models and on random effects model for capturing serial dependence of claims. We focus on the results using a family of elliptical copulas to model the dependence. The paper further describes how to calibrate the proposed model using illustrative claims data arising from a Singapore insurance company. The estimated results provide strong evidence of all forms of dependencies captured by our model.