Bayesian modelling, Monte Carlo sampling and capital allocation of insurance risks
Peters, Gareth W.
Targino, Rodrigo S.
Wüthrich, Mario V.
- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
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 Show more
Journal / seriesRisks
Subjectcapital allocation; premium and reserve risk; Solvency Capital Requirement (SCR); Sequential Monte Carlo (SMC); Swiss Solvency Test (SST)
Organisational unit08813 - Wüthrich, Mario Valentin (Tit.-Prof.)
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