Generating unfavourable VaR scenarios under Solvency II with patchwork copulas
Pfeifer Dietmar () and
Ragulina Olena ()
Additional contact information
Pfeifer Dietmar: Carl von Ossietzky Universität Oldenburg, Germany
Ragulina Olena: Taras Shevchenko National University of Kyiv, Ukraine
Dependence Modeling, 2021, vol. 9, issue 1, 327-346
Abstract:
The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union. Besides this, the Delegated Regulation by the European Commission requires all insurance companies under supervision to consider different risk scenarios in their risk management system for the company’s own risk assessment. Since it is unreasonable to assume that the potential worst case scenario will materialize in the company, we think that a modelling of various unfavourable scenarios as described in this paper is likewise appropriate. Our explicit copula approach can be considered as a special case of ordinal sums, which in two dimensions even leads to the technically worst VaR scenario.
Keywords: Solvency II; copulas; patchwork copulas; Bernstein copulas; Monte Carlo methods (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.1515/demo-2021-0115 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:9:y:2021:i:1:p:327-346:n:2
DOI: 10.1515/demo-2021-0115
Access Statistics for this article
Dependence Modeling is currently edited by Giovanni Puccetti
More articles in Dependence Modeling from De Gruyter
Bibliographic data for series maintained by Peter Golla ().