Rapidly bounding the exceedance probabilities of high aggregate losses

TL;DR

This paper evaluates six methods for estimating the probability of extreme aggregate losses in insurance, highlighting the effectiveness of the Moment bound and the Gamma distribution model for practical risk assessment.

Contribution

The paper introduces and compares six approaches for bounding high aggregate loss probabilities, emphasizing the Moment bound's efficiency and the Gamma distribution's flexibility.

Findings

Moment bound offers a good balance of tightness and speed.

Gamma distribution effectively models single event losses.

Cap on losses can be easily incorporated into models.

Abstract

We consider the task of assessing the righthand tail of an insurer's loss distribution for some specified period, such as a year. We present and analyse six different approaches: four upper bounds, and two approximations. We examine these approaches under a variety of conditions, using a large event loss table for US hurricanes. For its combination of tightness and computational speed, we favour the Moment bound. We also consider the appropriate size of Monte Carlo simulations, and the imposition of a cap on single event losses. We strongly favour the Gamma distribution as a flexible model for single event losses, for its tractable form in all of the methods we analyse, its generalisability, and because of the ease with which a cap on losses can be incorporated.