2. A N EW A PPROACH T O E DF V ALIDATION ISDA 2000

3. 2 VALIDATION BACKGROUND What is meant by Validation? Checking the impact of Methodology Assumptions on Result Reliability Why is it more of an issue now? Because of the new Basel Committee proposed framework

4. 3 VALIDATING METHODOLOGIES Common computations call on: Market Price assumptions Rating Judgment assumptions Historical Reference assumptions Economic Environment assumptions What are their limitations?

5. 4 VALIDATION CHALLENGE Statistical rules applied for market risks are inappropriate So validating default expectations requires to start with credit risk basics

6. 5 CORE VALIDATION ISSUES Can stable and consistent signals of default be predicted at least one year ahead of time? If yes, do these signals change over time? If yes, what are changes a function of?

7. 6 ADDRESSING VALIDATION ISSUES With the Default Filter (TM) Probability of Default computation methodology Methodology: “Model assisted numerical analysis” allowing macro-economic and specific company simulations Default: Missing committed payment after 3 months grace period

8. 7 VALIDATION DATA The issue with historical data: Choice of a good reference base Choice of time period Addressing missing data Addressing data reliability

9. 8 VALIDATION DATABASES.

10. 9 BUILDING REFERENCE DATABASES Driven by Distance and Homogeneity measures

11. 10 HOMOGENEITY OF COMPLEMENTARY DATA

12. 11 VALIDATION ELEMENTS. ACCURACYSTABILITY INDIVIDUAL COMPANIES PORTFOLIOS ALTERNATIVES JUDGEMENT

13. 12 6 RETAINED VALIDATION CRITERIA On randomly selected databases: Are defaulting companies predicted? Are results stable across databases? Are Portfolio default rates predicted? Is Accuracy due to data or methodology? Is the most significant factor stable? Do specific and economic stresses lead to sensible results?

14. 13 1. HISTORICAL BACK-TESTS Source: Default Filter system application Illustration:1000 Asian Private Corporations

15. 14 2. COMPARATIVE BACK-TESTS Source: Default Filter system application Illustration: Same data but alternative methodologies Default Filter No DefaultPortfolio Default

16. 15 3. STABILITY ACROSS DATABASES 0% 10% 20% 30% 40% 50% 60% 70% 0 - 5 %6 - 10 %11 - 15 %16 - 20 %21 -25 %Over 25 % Diversified Malaysian, Thai, Singapore Service Companies - Variation in probability of default across reference databases Percentage of portfolio Observed variation in probabilities of default (90% of the data tested) Source: Default Filter system application

17. 16 4. ACCURACY OF PORTFOLIO DEFAULT RATES Source: Default Filter system application

18. 17 5. STABILITY OF MOST SIGNIFICANT FACTOR Source: Default Filter system application QUICK RATIO FOR A PORTFOLIO OF NON-DEFAULTING UNLISTED MALAYSIAN CONSTRUCTION COMPANIES PLOTTED AGAINST RISK GRADUATION 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 GRADE 5 POD 21 - 25 % GRADE 4 POD 16 - 20 % GRADE 3 POD 11-15 % GRADE 2 POD 6 -10 % GRADE 1 POD 0-5 %

19. 18 6. SENSITIVITY TO CHANGES Source: Default Filter system application

20. 19 SUMMARISED VALIDATION BOARD Illustration for the Asian Electronic Manufacturing Industry Accuracy Total Default Weights Signs 94% 82% 6 Stability Portfolio Predicted Observed 13% 12% Significance Default Filter Default rate No Default Comparison 63% < 5% 72% < 10% 76% < 20% deviations [0.004,6.23] ok Factors Soundness

21. 20 SUMMARISED VALIDATION BOARD Illustration for the Asian Oil and Gas Industry Accuracy Total Default Factors Weights Signs 95% 60% 8 [-3.38,0.02] ok Stability 77% < 5% Portfolio Predicted Observed 7% 9% Significance Soundness 84% < 10% 91% < 20% Comparison deviations Default Filter No Default Default rate

22. 21 OPPORTUNITIES FOR RISK MANAGEMENT Compare Risk Signals between Homogeneous Groups Current Profile For 10% decrease in Sales Legend:

23. 22 1997 Probability of default of banks in Asia Under Normal Conditions Philippines Hong Kong Assess sensitivity to Macro-economic Changes Probability of Default TaiwanMalaysia Singapore Korea Thailand Indonesia 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Probability of bank default OPPORTUNITIES FOR RISK MANAGEMENT

24. 23 Taiwan Malaysia PhillipinesSingapore Korea HongKongThailandIndonesia Assess sensitivity to Macro-economic Changes Probability of Default Increase in default rate OPPORTUNITIES FOR RISK MANAGEMENT

25. 24 BUSINESS OPPORTUNITIES 1. Risk Communication tool to trade assets 2. Default Indices and sensitivity to economy 3. Contingent Pricing for Portfolios 4. Portfolio Insurance

26. 25 INDUSTRY INITIATIVE FOR BENCHMARKING DEFAULT RISK Internet Interface iqfinancial.com Credit Factor Input Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Save Input? Open Input? Future Environment in country GDP in sector growth in FX rate in interest rate Scenario Name Save Scenario? Open Scenario? Open Simulation? Probability of Default Rating Equivalent Validation Results? Save Probability? Validation Definition Sector Definition Factor Definition Rating Equivalent Definition Instructions Sector’s Default Profile