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									Current State of Credit Risk Measurement
Symposium on Enterprise Wide Risk Management Chicago, April 26, 2004

Peter O. Davis Partner, Ernst & Young LLP Director of Credit Risk Services


Continued Movement Toward Credit Quantification

Regulatory Push
Avoiding Unintended Consequences
Incidence vs. Dollar Based Default Rates


Continued Movement Towards Credit Quantification

Extending credit inherently a judgment-based decision Continued movement toward the reliance on credit models to support credit extension and portfolio management Driven in part by continued advances in credit risk modeling
More mature models Greater computing power Development of credit loss databases More credit products providing market information

Driven in part by demand for greater transparency
Large defaults by fallen angels triggered increased focus by investors Demand for greater information by senior management, Board, shareholders, rating agencies, regulators Demand for consistent measurement across products


Regulatory Push

For commercial banks, and (more recently) investment banks, regulators have created incentives for institutions to enhance their internal credit models For those meeting advanced standards, by year-end 2006, under “Basel II” regulators will rely upon institutions’ internal credit models for setting regulatory capital
Probability of default models
Loss given default models Exposure at default models

Will result in:
Standardization of credit risk measurement terminology and model classification Heavy focus on model accuracy Development of extensive credit performance databases, leading to ongoing innovations in model development Greater transparency in credit risk-taking across institutions Greater liquidity and continued innovation in credit products


Avoiding Unintended Consequences

As credit models are used more broadly across institutions and more deeply within institutions, continued need to challenge whether:
models accuracy capture risks model limitations are understood application of individuals models and the integration of multiple models produce results that are consistent with the intended measurement purpose

Example of the application of default models


Incidence-based vs. Dollar-based Defaults Illustration

Obligor default models measure the probability that an individual borrower will default over a given time horizon – an incidence measure of default risk When measuring expected loss (EL), it is common to use the product of the probability of default (PD), loss given default (LGD) and exposure at default (EAD) This approach implicitly assumes that incidence-based and dollar-based PDs are the same


Illustration Cont’d

Illustration of the impact of dollar-based vs. incidence-based default rates: Assumptions:
Three banks with loan portfolio of $100 million Same 10 borrowers and 3 defaults Obligors have same risk rating and incidence based default rates LGD = 100% for defaulted loans 100% closed-end loans


Illustration Cont’d

Bank A – Loan Size Does Not Differ
All the loans are the same size The dollar loss implied by the incidence-based default rate is the same as historical loss

Bank B – Large Positions in Loans to Defaulting Obligors
The dollar loss implied by the incidence-based default rate is based on the number of defaults and average value of the loans The size discrepancy of the loans are so large the average value hides more than it reveals

Bank C –Small Positions in Loans to Defaulting Obligors
The dollar loss is far lower than the incidence-based default rates imply


Loss Severity Adjustment

Loss Severity Adjustment is defined as the ratio of the average value of defaults to the average current balance of the portfolio For Bank B and C the use of incidence-based PD may not reflect the trends of the portfolio
For such instances a Loss Severity Adjustment may be applied to the losses implied by the incidence-based default rates After the adjustment, the dollar losses reflect historical figures and the trend in the portfolio towards higher or lower dollar-based default probabilities

Loss Severity Adjustment restores information lost in the averages by reconciling the incidence-based default rates with the banks loss in dollars


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