Bayesian modelling, Monte Carlo sampling and capital allocation of insurance risks
In: Risks, 5 (4, 2017
Online
academicJournal
Zugriff:
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.
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Bayesian modelling, Monte Carlo sampling and capital allocation of insurance risks
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Autor/in / Beteiligte Person: | Peters, Gareth W. ; Targino, Rodrigo S. ; Wüthrich, Mario V. |
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Zeitschrift: | Risks, 5 (4, 2017 |
Veröffentlichung: | MDPI, 2017 |
Medientyp: | academicJournal |
DOI: | 10.3929/ethz-b-000217495 |
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