Max-factor individual risk models with application to credit portfolios

Michel Denuit, Anna Kiriliouk, Johan Segers

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

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.

langue originaleAnglais
Pages (de - à)162-172
Nombre de pages11
journalInsurance: Mathematics and Economics
Volume62
Les DOIs
étatPublié - 1 mai 2015
Modification externeOui

Empreinte digitale

Linear Combination
Extreme Quantiles
Credit Risk
Loss Probability
Monotone Function
Diversification
Factor Models
Conditional probability
Model
Factors
Credit
Individual risk model
Modeling
Quantile
Credit risk

Citer ceci

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Max-factor individual risk models with application to credit portfolios. / Denuit, Michel; Kiriliouk, Anna; Segers, Johan.

Dans: Insurance: Mathematics and Economics, Vol 62, 01.05.2015, p. 162-172.

Résultats de recherche: Contribution à un journal/une revueArticle

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