Titre
An extreme value approach for modeling Operational Risk losses depending on covariates
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Chavez-Demoulin, V.
Auteure/Auteur
Embrechts, P.
Auteure/Auteur
Hofert, M.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
0022-4367
Statut éditorial
Publié
Date de publication
2016-09
Volume
83
Numéro
3
Première page
735
Dernière page/numéro d’article
776
Peer-reviewed
Oui
Langue
anglais
Résumé
A general methodology for modeling loss data depending on covariates is developed. The parameters of the frequency and severity distributions of the losses may depend on covariates. The loss frequency over time is modeled with a nonhomogeneous Poisson process with rate function depending on the covariates. This corresponds to a generalized additive model, which can be estimated with spline smoothing via penalized maximum likelihood estimation. The loss severity over time is modeled with a nonstationary generalized Pareto distribution (alternatively, a generalized extreme value distribution) depending on the covariates. Since spline smoothing cannot directly be applied in this case, an efficient algorithm based on orthogonal parameters is suggested. The methodology is applied both to simulated loss data and a database of operational risk losses collected from public media. Estimates, including confidence intervals, for risk measures such as Value-at-Risk as required by the Basel II/III framework are computed. Furthermore, an implementation of the statistical methodology in R is provided.
PID Serval
serval:BIB_D212C3A04FBD
Date de création
2014-08-12T11:47:21.510Z
Date de création dans IRIS
2025-05-21T03:59:22Z