Mesure des risques de march\'e et de souscription vie en situation d'information incompl\`ete pour un portefeuille de pr\'evoyance

TL;DR

This paper proposes a pragmatic stochastic modeling approach for measuring market and underwriting risks in life insurance portfolios with incomplete or aggregate data, aligning with new Embedded Value standards.

Contribution

It introduces a novel method for risk assessment in situations of incomplete information, addressing a gap in current stochastic modeling practices for life insurance portfolios.

Findings

Provides a practical modeling framework for incomplete data scenarios

Aligns risk measurement with MCEV standards

Enhances risk management for aggregated insurance portfolios

Abstract

In the framework of Embedded Value new standards, namely the MCEV norms, the latest principles published in June 2008 address the issue of market and underwriting risks measurement by using stochastic models of projection and valorization. Knowing that stochastic models particularly data-consuming, the question which can arise is the treatment of insurance portfolios only available in aggregate data or portfolios in situation of incomplete information. The aim of this article is to propose a pragmatic modeling of these risks tied up with death covers of individual protection products in these situations.