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Modelling Dependencies in Credit Risk Management
Company: ETH Zurich
Year Of Publication: 2000
Month Of Publication: November
Pages: 78
Download Count: 965
View Count: 7536
Comment Num: 0
Language: EN
Source:
Who Can Read: Free
Date: 6-18-2004
Publisher: Administrator
Summary
We commence with an overview of the three most widely usedcredit risk models developed by KMV, J.P. Morgan (CreditMetrics) andCredit Suisse First Boston (CreditRisk+). The mathematical essentialsof each model lie in the way the joint distribution of the so-called 'defaultindicators' is modeled, a vector of Bernoulli random variables.With the focus on these vectors we will investigate two general frameworksfor modelling such binary random events. We will also show howthe KMV and CreditMetrics methodology can be translated into theframework of CreditRisk+.The credit risk models are then compared for `homogeneous' portfoliosusing Monte Carlo simulation. As two of the three models usethe multivariate normal distribution for their `latent variables', we investigatethe impact when proceeding to the broader class of ellipticaldistributions. A so-called t-model, incorporating a t-copula for thelatent vector, shall be used to show the consequences of a possible generalisation.In this context we introduce the notion of tail dependence.Comparison of the extended t-model with the `normal' two credit riskmodels will again be performed for the same types of portfolios usedfor the previous comparison.Lastly, we will study the portfolio loss distributions for the variousmodels due to increased portfolio size.
Author(s)
Nyfeler, Mark A. Sign in to follow this author
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