CREDIT RATINGS oracles, miracles and much more Idzard van Eeghen ABNAMRO Bank ISDA/BBA/RMA Conference London June 23, 20

1. CREDIT RATINGSoracles, miracles and much moreIdzard van EeghenABNAMRO BankISDA/BBA/RMA Conference London June 23, 2003

2. WHAT THIS PRESENTATION WILL BE ABOUT • 1. Stakeholders and choices to be made • 2. Sovereign ratings • 3. Financial Institutions ratings • 4. Structured-product ratings

3. THE RATING SYSTEM AS ORACLE

4. THE RATING SYSTEM AS MIRACLE

5. RATINGS AND RATING VALIDATION: FOR WHOM AND TO WHAT PURPOSE? RATING SYSTEMS STAKEHOLDERS: • Supervisors, Portfolio Managers • Risk Management • Business Stakeholders have different and often conflicting needs, but have in common that they want credit ratings to be transparent.

6. WHAT IS THE OPTIMAL NUMBER OF RATING SYSTEMS? Differences in Accounting. E.g.: • IAS, country specific GAAP • Accountancy formats vary sometimes per Industry • Treatment of pension costs, salary costs, valuation issues Differences in risk drivers. E.g.: • country specific • industry specific (e.g. licences in Pharma, regulations in Public sector) • obligor specific  There is a natural tendency to increase the number of rating systems to infinity

7. NUMBER OF EXPLANATORY VARIABLES: SCYLLA OR CHARYBDIS? Scylla: the less data available the lower the number of model inputs that can be proven to be statistically relevant. Charybdis: the specialised (complex) nature of the obligor requires that many aspects be included if the rating model is to be credible to the users.

8. CHOICES MADE Criteria: • Transparency for all users • Intuitive/ logic • Quantification of risk  Range of rating systems, but optimal number is elusive  In each rating system, we group the many elements in meaningful “Pillars” which are perceived to be important drivers of risk  Rating models used as much for transparency reasons as for quantification of risk

9. SOVEREIGN FOREIGN-CURRENCY RATINGS Using external ratings for benchmarking and deriving default rates is problematic See for example: 1-year default rates for sovereigns: Moody’s 1985-2002 S&P: 1975-2002

10. SOVEREIGN LOCAL-CURRENCY RATINGS Sovereign local-currency ratings: difference between foreign currency and local-currency rating on average is: Moody’s: 0.4 notch S&P : 1.0 notch However: historic default rates of local-currency sovereign debt are only 1/10th of those of foreign-currency debt S&P expects Sovereign default rates to converge to those of Corporates. But aren’t Sovereign ratings in fact issue ratings? And is public debt comparable to bank debt?

11. FINANCIAL-INSTITUTIONS RATINGS Many obligors are investment-grade  Too few defaults to base default rates upon Benchmark not always available: e.g. hedge funds Information required for proper analysis is often sensitive for privacy or competitive reasons and therefore not disclosed

12. FINANCIAL-INSTITUTIONS RATINGS Two-sided Gauss-test, 95% confidence interval. Data: Moody’s Corporate Bond Default Database 1983-2000

13. RATINGS ARE MORE THAN 1-YEAR DEFAULT RATES  Behaviour of ratings in economic cycle.  Also, ratings have a “memory”. Look beyond 1-year default rates:  full migration matrix  longer periods

14. ANALYSING OUTLIERS: EXPERT JUDGEMENT REQUIRED In depth analysis is done when a model-generated rating shows a significant and/or systematic bias from external rating or internally approved rating. Determining the cause often requires expert judgement. E.g.

15. RATING STRUCTURED PRODUCTS Projects are often structured in such a way as to derive the desired external rating.  Band of observed rating categories becomes very limited  External rating determines the structure of the obligor/credit (“the tail wags the dog”) Benchmarking internal ratings with external ratings could lead to de facto copying of external rating “model”. Is that the purpose of your internal rating system?

16. FINALLY Ratings still have something of an oracle and of a miracle in them: they are a combination of man and machine.