Multivariate matrix-exponential affine mixtures and their applications in risk theory

Autor: Cheung, Eric C. K., Peralta, Oscar, Woo, Jae-Kyung
Rok vydání: 2021
Předmět:
Druh dokumentu: Working Paper
Popis: In this paper, a class of multivariate matrix-exponential affine mixtures with matrix-exponential marginals is proposed. The class is shown to possess various attractive properties such as closure under size-biased Esscher transform, order statistics, residual lifetime and higher order equilibrium distributions. This allows for explicit calculations of various actuarial quantities of interest. The results are applied in a wide range of actuarial problems including multivariate risk measures, aggregate loss, large claims reinsurance, weighted premium calculations and risk capital allocation. Furthermore, a multiplicative background risk model with dependent risks is considered and its capital allocation rules are provided as well. We finalize by discussing a calibration scheme based on complete data and potential avenues of research.
Databáze: arXiv