Lessons for Financial Risk Management from the Great Recession

1. Lessons for Financial Risk Management from the Great Recession David M. Rowe, Ph.D. EVP for Risk Management – SunGard Adaptiv Nykerdit Symposium 2009 Copenhagen, Denmark October 26, 2009

2. Sage Advice Learn from the mistakes of others, you'll never live long enough to make them all yourself. It’s also less painful than learning from your own mistakes.

3. The Lessons • Statistical Entropy • Structural Imagination • Self-Referential Feedback • Complexity and Second Order Uncertainty • Alternate Means of Valuation

4. 1. Statistical Entropy Data Information Statistical analysis can extract information from data, it cannot create information not already contained in the data. Stated more casually: Like water, information cannot rise higher than its source.

5. 1. Statistical Entropy I o n r o f i a t n m This is extraction of information, NOTcreation of information Data I o n r o f i a t n m Information

6. Extreme Confidence Estimates Alternately  1 annual default every century  1 annual default every 10,000 years! AAA AA A BBB BB B CCC Super-senior tranches of subprime mortgage-backed securities were rated AAA (or BETTER!!) What was the empirical basis for these ratings?

7. Mortgage Default Experience SOURCE: Mortgage Bankers Association - National Delinquency Survey

8. Hypothetical Detachment Point Hypothetical Subprime Default Probability Density 0.2500 0.2000 0.1500 Probability Density 0.1000 0.0500 - 0 5 10 15 20 25 Defaults (%) Log-Normal Distribution: Mean = 5.97; StDev = 2.16

9. Hypothetical Detachment Point Hypothetical Subprime Default Probability Density 0.2500 0.2000 0.1500 Probability Density 0.1000 0.0500 - 0 5 10 15 20 25 Defaults (%) .01% = AAA

10. Hypothetical Detachment Point Hypothetical Subprime Default Probability Density 0.2500 0.2000 0.1500 Probability Density 0.1000 0.0500 - 0 5 10 15 20 25 Defaults (%) Largest Sample Observation = 9.6% Behavior in the Tail is Based on What Distribution is Assumed .01% = AAA

11. 2. Structural Imagination Broad Geographic Distribution

12. 2. Structural Imagination Through mid-2006 Idiosyncratic Causes for Default What unobserved contingency could upset this pattern?

13. 2. Structural Imagination Threats to Diversification One candidate was fairly obvious. $ $ $ $ Falling housing prices would hurt ALL borrowers Defaults would no longer be statistically independent

14. 2. Structural Imagination 12-month % change Strongly Positive: 1995-2006 10 City Composite U.S. Home Price Index 12-month % change Jan-95 S&P/Case-Shiller Home Price Indices

15. 2. Structural Imagination Mar 1994 Aug 1990 12-month % change Negative for 3-1/2 years in early 1990s 10 City Composite U.S. Home Price Index 12-month % change S&P/Case-Shiller Home Price Indices

16. 2. Structural Imagination September 2005 Mar 1994 Aug 1990 Month-to-Month % Change Peaked in September 2005 : Turned Negative in mid-2006 10 City Composite U.S. Home Price Index 12-month % change Monthly % Change (annual rate) S&P/Case-Shiller Home Price Indices

17. 2. Structural Imagination The Lesson 1) Look for significant unrepresented variables. 2) Track these variables carefully as early warning indicators of emerging problems.

18. 3. Self-Referential Feedback The Seeds of Self-Destruction The huge expansion of subprime mortgage debt set the stage for a more serious crisis when conditions began to worsen.

19. An Explosion in Subprime Mortgage Originations 700 25% Over $600 billion and Over 20% of originations 600 20% 500 15% 400 300 10% 200 5% $150-$200 billion and 6% to 7% of originations 100 100 0 0% 2001 2002 2003 2004 2005 2006 2007 Subprime Mortgage Originations $ Billions Per year Percent Source: Inside Mortgage Finance By one estimate in late 2007, 14% of all outstanding mortgages were subprime

20. 3. Beware Self-Referential Feedback - 1 Risk Estimates Based on Historical Data Become Progressively Less Reliable   Growth in Volume   DARK RISK Achieving Greater Volume Required Relaxing Underwriting Standards   Further Innovations (e.g. Compound Repackaging, CDO2 ) Increased Complexity   A Unique Innovation Generated Attractive Returns

21. 3. Beware Self-Referential Feedback - 2 More Stringent Credit Conditions and Increased Liquidation Sales  Home Price Declines    CompoundEconomic Impact Defaults are Magnified by the Inflated Volume of Poorly Collateralized Mortgages    Credit Losses Hurt Bank Earnings  An Initial Economic Shock

22. 4. Complexity and Dark Risk Complexity Limited Data Dark Risk  +

23. 5. Alternate Means of Valuation Old Credit Risk Mantra What is the second means of repayment? Proposed Capital Markets Mantra What is the second means of valuation?

24. 5. Alternate Means of Valuation  

25. 5. Alternate Means of Valuation ?  

26. 5. Alternate Means of Valuation IRS CDS Corporate CDOs (2006) Subprime CDOs (2006) Ease of Current Valuation Level 1 Observable prices in active markets Observable prices in inactive markets or observable inputs to accepted pricing models Level 2 Few or no observable market prices and models requiring significant unobservable inputs Level 3

27. 5. Alternate Means of Valuation Effectiveness of Alternate Means of Valuation Level 2 Level 3 Level ? Level ? IRS Corporate CDOs (2006) Subprime CDOs (2006) CDS (2006) Level 1 Ease of Current Valuation CDS (2008) Level 2 Corporate CDOs (2008) Level 3 Subprime CDOs (2008)

28. Estimated US Banks Balance Sheet (2008 Q2) Subprime Related $540 Bill $11,950 Bill L i a b i l i t i e s A l l O t h e r A s s e t s $12,761 Bill Equity Subprime Related Assets ? Equity $1,351 Bill

29. A Question Was this crisis a Black Swan? ?

30. Elements of the Risk Puzzle (Original: May 2006) Technological Change Pace of Innovation Product Complexity Geopolitical Risk Liquidity Model Risk Commodity Prices Extreme Events Volume Growth External Linkages Emerging Markets Information Security Operational Risk Effective Portfolio Mgt. Regulatory Uncertainty Miscellaneous ???? Unknown Unknowns ????

31. Elements of the Risk Puzzle (Rev: October 2008) Pace of Innovation Product Complexity Technological Change Pace of Innovation Product Complexity Liquidity Model Risk Geopolitical Risk Liquidity Model Risk Volume Growth Commodity Prices Extreme Events Volume Growth External Linkages External Linkages Emerging Markets Information Security Operational Risk Effective Portfolio Mgt. Regulatory Uncertainty Miscellaneous ???? Unknown Unknowns ???? Commodity Prices Effective Portfolio Mgt.

32. Summary 1. Statistical Entropy 2. Structural Imagination Data Information • Use structural imagination to define significant unrepresented variables in existing risk analysis • Track these variables as early warning indicators

33. Summary     CompoundEconomic Impact     3. Self-Referential Feedback Recognize that success of an innovation can alter the environment in ways that jeopardize continued success

34. Summary 4. Complexity  Dark Risk  • Limited data and untested complexity make risk estimates inherently uncertain

35. Summary 5. Second Means of Valuation ?   Limit holdings of assets with no reasonably objective second means of valuation even if they are highly liquid today

36. A Final Thought – Strategic Risk Board of Directors Where the buck stops CEO “Give me 15% more than last year. Don’t give me excuses, give me the numbers.” ≠ sound aggressive management = recipe for disaster