Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks

Gareth W. Peters, Rodrigo Targino and Mario V. Wüthrich
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Gareth W. Peters: Department of Statistical Science, University College London, London WC1E 6BT, UK
Mario V. Wüthrich: RiskLab, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland

Risks, 2017, vol. 5, issue 4, 1-51

Abstract: 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.

Keywords: capital allocation; premium and reserve risk; Solvency Capital Requirement (SCR); Sequential Monte Carlo (SMC); Swiss Solvency Test (SST) (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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