Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks
AbstractThe 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. View Full-Text
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Peters, G.W.; Targino, R.S.; Wüthrich, M.V. Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks. Risks 2017, 5, 53.
Peters GW, Targino RS, Wüthrich MV. Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks. Risks. 2017; 5(4):53.Chicago/Turabian Style
Peters, Gareth W.; Targino, Rodrigo S.; Wüthrich, Mario V. 2017. "Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks." Risks 5, no. 4: 53.