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J. Risk Financial Manag. 2016, 9(1), 1; doi:10.3390/jrfm9010001

The Two Defaults Scenario for Stressing Credit Portfolio Loss Distributions

Bank of England, Threadneedle Street, London EC2R 8AH, UK
Academic Editors: Stefan Mittnik and Marc S. Paolella
Received: 27 October 2015 / Revised: 18 November 2015 / Accepted: 24 November 2015 / Published: 31 December 2015
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
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Abstract

The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes impractical for two or more simultaneous defaults as then the conditioning event is extremely rare. We provide an analytical approach to the calculation of the conditional loss distribution for the CreditRisk + portfolio model with independent random loss given default distributions. The analytical solution for this case can be used to check the accuracy of an approximation to the conditional loss distribution whereby the unconditional model is run with stressed input probabilities of default (PDs). It turns out that this approximation is unbiased. Numerical examples, however, suggest that the approximation may be seriously inaccurate but that the inaccuracy leads to overestimation of tail losses and, hence, the approach errs on the conservative side. View Full-Text
Keywords: CreditRisk+; stress test; scenario analysis; joint default probability CreditRisk+; stress test; scenario analysis; joint default probability
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tasche, D. The Two Defaults Scenario for Stressing Credit Portfolio Loss Distributions. J. Risk Financial Manag. 2016, 9, 1.

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