Corrected phase-type approximations of heavy-tailed risk models using perturbation analysis

TL;DR

This paper develops highly accurate corrected phase-type approximations for performance measures in heavy-tailed risk models, using perturbation analysis to combine phase-type and heavy-tailed distributions for improved numerical evaluation.

Contribution

It introduces corrected phase-type approximations derived from perturbation series, enhancing accuracy in heavy-tailed risk models beyond traditional methods.

Findings

Approximations achieve small absolute and relative errors.

Effective for both finite and infinite time horizons.

Numerical experiments confirm high accuracy.

Abstract

Numerical evaluation of performance measures in heavy-tailed risk models is an important and challenging problem. In this paper, we construct very accurate approximations of such performance measures that provide small absolute and relative errors. Motivated by statistical analysis, we assume that the claim sizes are a mixture of a phase-type and a heavy-tailed distribution and with the aid of perturbation analysis we derive a series expansion for the performance measure under consideration. Our proposed approximations consist of the first two terms of this series expansion, where the first term is a phase-type approximation of our measure. We refer to our approximations collectively as corrected phase-type approximations. We show that the corrected phase-type approximations exhibit a nice behavior both in finite and infinite time horizon, and we check their accuracy through numerical experiments.