Incorporating contagion in portfolio credit risk models using network theory
type: Item
instance of: scholarly article
title: Incorporating contagion in portfolio credit risk models using network theory
zbMATH Open document ID: 1380.91138
DOI: 10.1155/2018/6076173
author: Ioannis Anagnostou
author: Sumit Sourabh
author: Drona Kandhai
published in: Complexity
publication date: 2018-01-29T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 91G40
Mathematics Subject Classification ID: 90B15
zbMATH DE Number: 6830340
zbMATH Keywords: portfolio credit risk models
zbMATH Keywords: network theory
zbMATH Keywords: conditional independence framework
zbMATH Keywords: correlation
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/6076173
OpenAlex ID: W2782748083
cites work: Q5253267
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description: scientific article; zbMATH DE number 6830340