Modeling multivariate operational losses via copula-based distributions with g-and-h marginals

Bee, Marco; Hambuckers, Julien

doi:10.21314/JOP.2021.016

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Modeling multivariate operational losses via copula-based distributions with g-and-h marginals

Bee, Marco; Hambuckers, Julien

2022 • In Journal of Operational Risk

Peer reviewed

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https://hdl.handle.net/2268/263620

DOI
10.21314/JOP.2021.016

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Keywords :

Loss model; dependence structure; vine copula; value-at-risk

Abstract :

[en] We propose a family of copula-based multivariate distributions with g-and-h marginals. After studying the properties of the distribution, we develop a two-step estimation strategy and analyze via simulation the sampling distribution of the estimators. The methodology is used for the analysis of a 7-dimensional dataset containing 40,871 operational losses. The empirical evidence suggests that a distribution based on a single copula is not flexible enough, thus we model the dependence structure by means of vine copulas. We show that the approach based on regular vines improves the fi t. Moreover, even though losses corresponding to different event types are found to be dependent, the assumption of perfect positive dependence is not supported by our analysis. As a result, the Value-at-Risk of the total operational loss distribution obtained from the copula- based technique is substantially smaller at high confidence levels, with respect to the one obtained using the common practice of summing the univariate Value-at-Risks.

Disciplines :

Quantitative methods in economics & management

Author, co-author :

Bee, Marco

Hambuckers, Julien ; Université de Liège - ULiège > HEC Liège : UER > UER Finance et Droit : Finance de Marché

Language :

English

Title :

Modeling multivariate operational losses via copula-based distributions with g-and-h marginals

Publication date :

March 2022

Journal title :

Journal of Operational Risk

ISSN :

1744-6740

eISSN :

1755-2710

Publisher :

Risk.net, United Kingdom

Peer reviewed :

Peer reviewed

Available on ORBi :

since 21 September 2021

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