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  • 标题:Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks
  • 本地全文:下载
  • 作者:Peters, Gareth W. ; Targino, Rodrigo S. ; Wüthrich, Mario V.
  • 期刊名称:Risks
  • 印刷版ISSN:2227-9091
  • 出版年度:2017
  • 卷号:5
  • 期号:4
  • 页码:1-51
  • 出版社:MDPI, Open Access Journal
  • 摘要: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.
  • 关键词:capital allocation; premium and reserve risk; Solvency Capital Requirement (SCR); Sequential Monte Carlo (SMC); Swiss Solvency Test (SST)
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