Statistics of Extremes for the Insurance Industry

TL;DR

This paper surveys extreme value modeling techniques relevant to insurance, discussing their adaptation and application to real data, including methods like truncation, tempering, censoring, and regression.

Contribution

It provides a comprehensive overview of extreme value techniques tailored for insurance, highlighting their practical adaptation and illustrating with concrete data examples.

Findings

Techniques like truncation and tempering are crucial for modeling insurance extremes.

Regression methods help in understanding risk factors in extreme events.

Illustrations demonstrate practical application on real insurance data.

Abstract

We provide a survey of how techniques developed for the modelling of extremes naturally matter in insurance, and how they need to and can be adapted for the insurance applications. Topics covered include truncation, tempering, censoring and regression techniques. The discussed techniques are illustrated on concrete data sets.