Bayesian modelling, Monte Carlo sampling and capital allocation of insurance risks

Artikel

The main objective of this work is to develop a detailed step-by-step guide to the development and application of a new class of efficient Monte Carlo methods to solve practically important problems faced by insurers under the new solvency regulations. In particular, a novel Monte Carlo method to calculate capital allocations for a general insurance company is developed, with a focus on coherent capital allocation that is compliant with the Swiss Solvency Test. The data used is based on the balance sheet of a representative stylized company. For each line of business in that company, allocations are calculated for the one-year risk with dependencies based on correlations given by the Swiss Solvency Test. Two different approaches for dealing with parameter uncertainty are discussed and simulation algorithms based on (pseudo-marginal) Sequential Monte Carlo algorithms are described and their efficiency is analysed.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Risks ; ISSN: 2227-9091 ; Volume: 5 ; Year: 2017 ; Issue: 4 ; Pages: 1-51 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: capital allocation ; premium and reserve risk ; Solvency Capital Requirement (SCR) ; Sequential Monte Carlo (SMC) ; Swiss Solvency Test (SST)

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Peters, Gareth W. ; Targino, Rodrigo S. ; Wüthrich, Mario V.
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2017

Further information

• DOI: doi:10.3390/risks5040053
• Handle: http://hdl.handle.net/10419/195782
• Last update: 10.03.2025, 11:46 AM CET