Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory
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 su...
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Online Access: | http://dx.doi.org/10.1155/2018/6076173 |
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doaj-933823d086d449be9e57984ecfff837e2020-11-24T20:49:14ZengHindawi-WileyComplexity1076-27871099-05262018-01-01201810.1155/2018/60761736076173Incorporating Contagion in Portfolio Credit Risk Models Using Network TheoryIoannis Anagnostou0Sumit Sourabh1Drona Kandhai2Computational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, NetherlandsComputational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, NetherlandsComputational Science Lab, University of Amsterdam, Science Park 904, 1098XH Amsterdam, NetherlandsPortfolio 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.http://dx.doi.org/10.1155/2018/6076173 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ioannis Anagnostou Sumit Sourabh Drona Kandhai |
spellingShingle |
Ioannis Anagnostou Sumit Sourabh Drona Kandhai Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory Complexity |
author_facet |
Ioannis Anagnostou Sumit Sourabh Drona Kandhai |
author_sort |
Ioannis Anagnostou |
title |
Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory |
title_short |
Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory |
title_full |
Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory |
title_fullStr |
Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory |
title_full_unstemmed |
Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory |
title_sort |
incorporating contagion in portfolio credit risk models using network theory |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1076-2787 1099-0526 |
publishDate |
2018-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2018/6076173 |
work_keys_str_mv |
AT ioannisanagnostou incorporatingcontagioninportfoliocreditriskmodelsusingnetworktheory AT sumitsourabh incorporatingcontagioninportfoliocreditriskmodelsusingnetworktheory AT dronakandhai incorporatingcontagioninportfoliocreditriskmodelsusingnetworktheory |
_version_ |
1716806366303092736 |