Tail behaviour of credit loss distributions for general latent factor models

A. Lucas; P. Klaassen; S.T.M. Straetmans; P.J.C. Spreij

doi:10.1080/1350486032000160786

Tail behaviour of credit loss distributions for general latent factor models

A. Lucas
, P. Klaassen
, S.T.M. Straetmans
, P.J.C. Spreij

Finance

Research output: Contribution to Journal › Article › Academic

Abstract

Using a limiting approach to portfolio credit risk, we obtain analytic expressions for the tail behavior of credit losses. To capture the co-movements in defaults over time, we assume that defaults are triggered by a general, possibly non-linear, factor model involving both systematic and idiosyncratic risk factors. The model encompasses default mechanisms in popular models of portfolio credit risk, such as CreditMetrics and CreditRisk

Original language	English
Pages (from-to)	337-357
Journal	Applied Mathematical Finance
DOIs	https://doi.org/10.1080/1350486032000160786
Publication status	Published - 2003

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abstract = "Using a limiting approach to portfolio credit risk, we obtain analytic expressions for the tail behavior of credit losses. To capture the co-movements in defaults over time, we assume that defaults are triggered by a general, possibly non-linear, factor model involving both systematic and idiosyncratic risk factors. The model encompasses default mechanisms in popular models of portfolio credit risk, such as CreditMetrics and CreditRisk",

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AB - Using a limiting approach to portfolio credit risk, we obtain analytic expressions for the tail behavior of credit losses. To capture the co-movements in defaults over time, we assume that defaults are triggered by a general, possibly non-linear, factor model involving both systematic and idiosyncratic risk factors. The model encompasses default mechanisms in popular models of portfolio credit risk, such as CreditMetrics and CreditRisk

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SN - 1350-486X

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JO - Applied Mathematical Finance

JF - Applied Mathematical Finance

ER -

Tail behaviour of credit loss distributions for general latent factor models

Abstract

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