Identifying European industries with extreme default risk: application of CVaR techniques to transition matrices

D. E. Allen, A. R. Kramadibrata, R. J. Powell, A. K. Singh

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Transition matrices help lenders to analyze credit risk by identifying the probability of a loan (or portfolio of loans) transitioning from one risk grade to another, including the risk of transitioning to a default grade.Traditionally, transition matrices have been used to measure credit Value at Risk (VaR), which is an estimate of losses below a selected threshold. Conditional Value at Risk (CVaR) measures the extreme risk beyond VaR. We use enhanced transition matrix CVaR techniques to measure the relative credit risk of ten European industries. This helps lenders identify those industries exposed to extreme default risk. The paper finds no correlation between VaR and CVaR metrics, meaning that VaR techniques fail to adequately identify the most risky industries which are most likely to experience defaults in times of extreme risk.The CVaR techniques, on the other hand, do identify this extreme industry risk. Over concentration in high risk industries can contribute to bank losses. The techniques in this study can assist lenders in identifying high risk industries which may need additional provisions,capital or industry exposure limits.
LanguageEnglish
Pages46-58
Number of pages13
JournalWorld review of business research
Volume2
Issue number6
Publication statusPublished - Nov 2012
Externally publishedYes

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Industry
Conditional value at risk
Default risk
Transition matrix
Value at risk
Credit risk
Loans
Credit
Risk measures

Cite this

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title = "Identifying European industries with extreme default risk: application of CVaR techniques to transition matrices",
abstract = "Transition matrices help lenders to analyze credit risk by identifying the probability of a loan (or portfolio of loans) transitioning from one risk grade to another, including the risk of transitioning to a default grade.Traditionally, transition matrices have been used to measure credit Value at Risk (VaR), which is an estimate of losses below a selected threshold. Conditional Value at Risk (CVaR) measures the extreme risk beyond VaR. We use enhanced transition matrix CVaR techniques to measure the relative credit risk of ten European industries. This helps lenders identify those industries exposed to extreme default risk. The paper finds no correlation between VaR and CVaR metrics, meaning that VaR techniques fail to adequately identify the most risky industries which are most likely to experience defaults in times of extreme risk.The CVaR techniques, on the other hand, do identify this extreme industry risk. Over concentration in high risk industries can contribute to bank losses. The techniques in this study can assist lenders in identifying high risk industries which may need additional provisions,capital or industry exposure limits.",
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Identifying European industries with extreme default risk : application of CVaR techniques to transition matrices. / Allen, D. E.; Kramadibrata, A. R.; Powell, R. J.; Singh, A. K.

In: World review of business research, Vol. 2, No. 6, 11.2012, p. 46-58.

Research output: Contribution to journalArticleResearchpeer-review

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