Conditional Least Squares and Copulae in Claims Reserving for a Single Line of Business

TL;DR

This paper introduces a novel claims reserving model combining conditional least squares and copulae, allowing for more accurate reserve distribution estimation by modeling dependence and reducing parameter complexity.

Contribution

It proposes a generalized time series model with dependence modeling and parameter efficiency, improving claims reserving accuracy over classical methods.

Findings

Model achieves more precise reserve forecasts.

Dependence modeling via copulae enhances distribution estimates.

Parameter count remains constant regardless of development periods.

Abstract

One of the main goals in non-life insurance is to estimate the claims reserve distribution. A generalized time series model, that allows for modeling the conditional mean and variance of the claim amounts, is proposed for the claims development. On contrary to the classical stochastic reserving techniques, the number of model parameters does not depend on the number of development periods, which leads to a more precise forecasting. Moreover, the time series innovations for the consecutive claims are not considered to be independent anymore. Conditional least squares are used for model parameter estimation and consistency of such estimate is proved. Copula approach is used for modeling the dependence structure, which improves the precision of the reserve distribution estimate as well. Real data examples are provided as an illustration of the potential benefits of the presented approach.