Stable-1/2 Bridges and Insurance

TL;DR

This paper introduces a novel non-life reserving model using stable-1/2 random bridges, enabling flexible ultimate loss distribution modeling and explicit calculation of best-estimate reserves, including for catastrophic risks.

Contribution

It develops a new class of reserving models based on stable-1/2 random bridges with explicit formulas and extensions for multiple dependent claims processes.

Findings

Explicit formulas for the best-estimate ultimate loss process.

Modeling of claims with non-trivial catastrophic loss probability.

Low-dimensional calculations for multi-line or multi-year models.

Abstract

We develop a class of non-life reserving models using a stable-1/2 random bridge to simulate the accumulation of paid claims, allowing for an essentially arbitrary choice of a priori distribution for the ultimate loss. Taking an information-based approach to the reserving problem, we derive the process of the conditional distribution of the ultimate loss. The "best-estimate ultimate loss process" is given by the conditional expectation of the ultimate loss. We derive explicit expressions for the best-estimate ultimate loss process, and for expected recoveries arising from aggregate excess-of-loss reinsurance treaties. Use of a deterministic time change allows for the matching of any initial (increasing) development pattern for the paid claims. We show that these methods are well-suited to the modelling of claims where there is a non-trivial probability of catastrophic loss. The generalized inverse-Gaussian (GIG) distribution is shown to be a natural choice for the a priori ultimate loss distribution. For particular GIG parameter choices, the best-estimate ultimate loss process can be written as a rational function of the paid-claims process. We extend the model to include a second paid-claims process, and allow the two processes to be dependent. The results obtained can be applied to the modelling of multiple lines of business or multiple origin years. The multi-dimensional model has the property that the dimensionality of calculations remains low, regardless of the number of paid-claims processes. An algorithm is provided for the simulation of the paid-claims processes.