Max-factor individual risk models with application to credit portfolios

Individual risk models need to capture possible correlations as failing to do so typically results in an underestimation of extreme quantiles of the aggregate loss. Such dependence modelling is particularly important for managing credit risk, for instance, where joint defaults are a major cause of concern. Often, the dependence between the individual loss occurrence indicators is driven by a small number of unobservable factors. Conditional loss probabilities are then expressed as monotone functions of linear combinations of these hidden factors. However, combining the factors in a linear way allows for some compensation between them. Such diversification effects are not always desirable and this is why the present work proposes a new model replacing linear combinations with maxima. These max-factor models give more insight into which of the factors is dominant.

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Document type	Article de périodique (Journal article) – Article de recherche
Access type	Accès interdit
Publication date	2015
Language	Anglais
Journal information	"Insurance: Mathematics and Economics" - Vol. 62, p. 162-172 (2015)
Peer reviewed	yes
Publisher	Elsevier BV * North-Holland (Amsterdam)
issn	0167-6687
e-issn	1873-5959
Publication status	Publié
Affiliation	UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Links	http://hdl.handle.net/2078.1/159060[Handle] https://doi.org/10.1016/j.insmatheco.2015.03.006[DOI]

See also
	[boreal:154837]	Max-Factor individual risk models with application to credit portfolios