Quantifying Life Insurance Risk using Least-Squares Monte Carlo

Claus Baumgart, Johannes Krebs, Robert Lempertseder and Oliver Pfaffel

Papers from arXiv.org

Abstract: This article presents a stochastic framework to quantify the biometric risk of an insurance portfolio in solvency regimes such as Solvency II or the Swiss Solvency Test (SST). The main difficulty in this context constitutes in the proper representation of long term risks in the profit-loss distribution over a one year horizon. This will be resolved by using least-squares Monte Carlo methods to quantify the impact of new experience on the annual re-valuation of the portfolio. Therefore our stochastic model can be seen as an example for an internal model, as allowed under Solvency II or the SST. Since our model does not rely upon nested simulations it is computationally fast and easy to implement.

Date: 2019-10
New Economics Papers: this item is included in nep-ias, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Der Aktuar 02.2019

Downloads: (external link)
http://arxiv.org/pdf/1910.03951 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1910.03951

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().