Credit Risk In A Model World

1. Credit Risk In A Model World Bob Scanlon

2. Credit Risk In A Model World • Backtesting • Volatility of capital • Database • Rating requirements • Consistency of rating • Parameters for portfolio risk

3. Impact of Loan Portfolio MTM • Introduction: • The movement towards marking to market loan portfolios will have a significant impact on P&L volatility. • The principle drivers will be; • volatility in credit spreads. • the nature of the portfolio, in terms of credit rating and the tenor of the loans.

4. Impact of Loan Portfolio MTM • A hypothetical 5 year $25bn loan portfolio has been modelled to show the impact of changes in credit spreads;

5. Use in Credit Assessment RATING MODELS HAVE NOW BEEN DEVELOPED SUFFICIENTLY TO BE USED FOR STAND-ALONE RATINGS • Benefits of using robust, well-validated rating models • Consistent: All factors inherent in ratings are imputed into final ratings via universally-accepted benchmarks • Unbiased: Subjective judgement can be consistently applied within the rating process. • Transparent: Models provide a complete description of methodology employed • Coverage: Ability to assess corporates and banks beyond coverage of the major rating agencies. • Efficiency: Ratings can be quickly generated using extensive on-line databases.

6. Implementation CRS EMPLOYS ON-LINE FINANCIAL DATA TO GENERATE RATINGS BASED ON A PROVEN RATING METHODOLOGY CRS is an integrated system

7. Sample Output

8. Process of Development DEVELOPMENT OF CRS IS AN EVOLUTIONARY PROCESS, AND NOT PURELY QUANTITATIVE • Development involves significant analytical evaluation and feedback

9. Methodology CHOICE OF APPROACH IS BASED ON SEVERAL CRITERIA, BUT MUST BE SIMPLE !! • Quantitative models must be supportive to the analysis of credit risk • Cannot be a ‘black box’ – needs to be sufficiently transparent to allow interpretation of output • Need for compatibility with benchmarks used within internal rating process

10. Approaches Used CRS IS A HYBRID APPROACH WITH MODELS DEFINED BY SECTOR AND JURISDICTION • Input to the models

11. Fundamental Data HIGH CORRELATIONS BETWEEN VARIABLES ALLOW DEVELOPMENT OF SIMPLE, BUT EFFECTIVE MODELS Example - US food retailing

12. Fundamental Data NON-LINEAR METHODS ARE NECESSARY FOR OPTIMAL MODEL PERFORMANCE Example - Profits and Financial Strength Rating for European Banks

13. Model Validation CRS HAS BEEN VALIDATED USING SEVERAL APPROACHES, RATHER THAN A SIMPLE “ONE ANSWER” APPROACH Comprehensive validation should employ a multi-faceted approach

14. Default Prediction CRS DEFAULT EXPERIENCE • Analysis of rated defaults shows similar ratings at ‘near default’ • Correspondence between CRS and public ratings by looking at ratings for a portfolio of names one year prior to default. Source: Moody’s Default Risk Service

15. Impact of Size STABILITY OF KEY DRIVERS, SUCH AS SIZE, IS CRITICAL TO USE ON DISPARATE PORTFOLIOS Example – Impact of asset size on model performance for chemicals

16. Credit Risk In A Model World • Once adopted how do you integrate model use for • Credit decisions • Exposure methodology • Profit / risk maximisation minimisation or risk/reward • Control or business function

17. Credit Risk In A Model World • Limit homogenisation • Weighted approach • Benchmarks • New deals into portfolio • Immunisation • Credit derivatives ? • Next generation

18. Credit Risk In A Model World • Out-performance through monitoring • Parameter adjustment • Staffing levels • Information inputs • Fall back process • Prayers