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Volume 2018 (2018), Article ID 6076173, 15 pages
Research Article

Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory

1Computational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, Netherlands
2Quantitative Analytics, ING Bank, Foppingadreef 7, 1102BD Amsterdam, Netherlands

Correspondence should be addressed to Ioannis Anagnostou

Received 20 September 2017; Accepted 29 November 2017; Published 8 January 2018

Academic Editor: Thiago C. Silva

Copyright © 2018 Ioannis Anagnostou et al. 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.


Portfolio credit risk models estimate the range of potential losses due to defaults or deteriorations in credit quality. Most of these models perceive default correlation as fully captured by the dependence on a set of common underlying risk factors. In light of empirical evidence, the ability of such a conditional independence framework to accommodate for the occasional default clustering has been questioned repeatedly. Thus, financial institutions have relied on stressed correlations or alternative copulas with more extreme tail dependence. In this paper, we propose a different remedy—augmenting systematic risk factors with a contagious default mechanism which affects the entire universe of credits. We construct credit stress propagation networks and calibrate contagion parameters for infectious defaults. The resulting framework is implemented on synthetic test portfolios wherein the contagion effect is shown to have a significant impact on the tails of the loss distributions.