### 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 originale | Anglais |
---|---|

Pages (de - à) | 162-172 |

Nombre de pages | 11 |

journal | Insurance: Mathematics and Economics |

Volume | 62 |

Les DOIs | |

état | Publié - 1 mai 2015 |

Modification externe | Oui |

### Empreinte digitale

### Citer ceci

*Insurance: Mathematics and Economics*,

*62*, 162-172. https://doi.org/10.1016/j.insmatheco.2015.03.006

}

*Insurance: Mathematics and Economics*, VOL. 62, p. 162-172. https://doi.org/10.1016/j.insmatheco.2015.03.006

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

Résultats de recherche: Contribution à un journal/une revue › Article

TY - JOUR

T1 - Max-factor individual risk models with application to credit portfolios

AU - Denuit, Michel

AU - Kiriliouk, Anna

AU - Segers, Johan

PY - 2015/5/1

Y1 - 2015/5/1

N2 - 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.

AB - 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.

KW - Calibration

KW - Default indicator

KW - Dependence modelling

KW - Latent factors

KW - Loss occurrence

UR - http://www.scopus.com/inward/record.url?scp=84926475705&partnerID=8YFLogxK

U2 - 10.1016/j.insmatheco.2015.03.006

DO - 10.1016/j.insmatheco.2015.03.006

M3 - Article

AN - SCOPUS:84926475705

VL - 62

SP - 162

EP - 172

JO - Insurance: Mathematics and Economics

JF - Insurance: Mathematics and Economics

SN - 0167-6687

ER -