Multivariate VaRs for Operational Risk Capital Computation: a Vine Structure Approach

Dominique Guegan () and Bertrand Hassani ()
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Dominique Guegan: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
Bertrand Hassani: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: The Basel Advanced Measurement Approach requires financial institutions to compute capital requirements on internal data sets. In this paper we introduce a new methodology permitting capital requirements to take into account the embedded dependence structures of operational risks. The loss distributions are provided in a matrix of 56 cells. Constructing a vine architecture, which is a bivariate decomposition of a n-dimensional structure (n > 2), we use this approach to compute multivariate operational risk VaRs. We analyse the results and compare them with classical methodologies based on LDF modelings. Our method is simple to carry out, easy to interpret and complies with the new Basel Committee requirements.

Keywords: VaR; nested structure; vine copula; loss distribution function; Operational risks; copules; distribution de perte; Risques opérationnels (search for similar items in EconPapers)
Date: 2012-04
New Economics Papers: this item is included in nep-ban, nep-ecm and nep-rmg
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00587706v3
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Published in 2012

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Related works:
Working Paper: Multivariate VaRs for Operational Risk Capital Computation: a Vine Structure Approach (2011)
Working Paper: Multivariate VaRs for Operational Risk Capital Computation: a Vine Structure Approach (2011)
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