Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks
53 Pages Posted: 3 May 2017
Date Written: May 2, 2017
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 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)
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