Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-moments

TL;DR

This paper introduces a new robust and efficient L-moments based estimation method for Tukey transform loss models in non-life insurance, enabling flexible skewness, kurtosis, and explicit quantile specifications for risk measurement.

Contribution

It develops a novel estimation procedure for Tukey transform models using L-moments, improving robustness and efficiency over existing methods.

Findings

The proposed method outperforms current estimators in robustness and efficiency.

It simplifies practical implementation for loss model fitting.

The models allow explicit quantile specification for risk measures.

Abstract

This paper discusses different classes of loss models in non-life insurance settings. It then overviews the class Tukey transform loss models that have not yet been widely considered in non-life insurance modelling, but offer opportunities to produce flexible skewness and kurtosis features often required in loss modelling. In addition, these loss models admit explicit quantile specifications which make them directly relevant for quantile based risk measure calculations. We detail various parameterizations and sub-families of the Tukey transform based models, such as the g-and-h, g-and-k and g-and-j models, including their properties of relevance to loss modelling. One of the challenges with such models is to perform robust estimation for the loss model parameters that will be amenable to practitioners when fitting such models. In this paper we develop a novel, efficient and robust estimation procedure for estimation of model parameters in this family Tukey transform models, based on L-moments. It is shown to be more robust and efficient than current state of the art methods of estimation for such families of loss models and is simple to implement for practical purposes.