DefaultRisk.com the web's biggest credit risk modeling resource. |  |
|
|
| Identifying European Industries with Extreme Default Risk: Application of CVaR techniques to transition matrices by David E. Allen of Edith Cowan University, November 2012 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. Published in: World Review of Business Research, Vol. 2, No. 6, (November 2012), pp. 46-58. Previously titled: Enhancing Transition Matrices to Measure Extreme Credit Risk in Europe Books Referenced in this paper: (what is this?)
Download paper (524K PDF) 13 pages Most Cited Books within Credit Modeling Papers [ |
