Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195782 
Year of Publication: 
2017
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 5 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-51
Publisher: 
MDPI, Basel
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.
Subjects: 
capital allocation
premium and reserve risk
Solvency Capital Requirement (SCR)
Sequential Monte Carlo (SMC)
Swiss Solvency Test (SST)
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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