Max-factor individual risk models with application to credit portfolios

Michel Denuit, Anna Kiriliouk, Johan Segers

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)162-172
Number of pages11
JournalInsurance: Mathematics and Economics
Publication statusPublished - 1 May 2015
Externally publishedYes


  • Calibration
  • Default indicator
  • Dependence modelling
  • Latent factors
  • Loss occurrence


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