Solvency II and diversification effect for non-life premium and reserves risk: new results based on non-parametric copulas

To increase the safety of the insured, the Solvency II regulation of 2016 introduces the obligation to determine the Solvency Capital Requirement. The standard approach consists in aggregating the capital requirements for individual risk types, based on the dependence structure described by the corr...

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Bibliographic Details
Published in:Risk management (Leicestershire, England) England), 2023-09, Vol.25 (3), p.20, Article 20
Main Authors: Szczęsny, Krystian, Wanat, Stanisław, Denkowska, Anna
Format: Article
Language:English
Subjects:
Comparative studies
Correlation analysis
Dependence
Diversification
Economics and Finance
Finance
Modelling
Nonparametric statistics
Original Article
Parameter estimation
Prior knowledge
Random variables
Regulation
Risk
Risk Management
Safety regulations
Solvency
Underwriting
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Summary:To increase the safety of the insured, the Solvency II regulation of 2016 introduces the obligation to determine the Solvency Capital Requirement. The standard approach consists in aggregating the capital requirements for individual risk types, based on the dependence structure described by the correlation matrix established in the Directive. A pan-European comparative study was launched in October 2020 with one aim to better understand the dependence between, on the one side, the interdependencies modeling approaches and risk aggregation and, on the other, the resulting diversification benefits. In the present article, we will present a new method of non-parametric estimation of the quantile of the sum of the dependent insurer’s risk types. The diversification effect obtained from the dependencies with the use of these non-parametric copulas is higher than the diversification effect obtained by using the dependence based on vine copulas. This method makes it possible to determine the structure of dependencies more flexibly in the absence of prior knowledge about the stochastic properties of risk-modeling variables and for a limited number of observations.
ISSN:1460-3799
1743-4637
DOI:10.1057/s41283-023-00125-1