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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