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. Sage Advice. Learn from the mistakes of others,
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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
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.
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.
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This is extraction of information, NOTcreation of information
Data
I
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Information
Alternately
1 annual default every century
1 annual default every 10,000 years!
AAA
AA
A
BBB
BB
B
CCC
Supersenior tranches of subprime mortgagebacked securities were rated AAA (or BETTER!!)
What was the empirical basis for these ratings?
SOURCE: Mortgage Bankers Association  National Delinquency Survey
Hypothetical Subprime Default Probability Density
0.2500
0.2000
0.1500
Probability Density
0.1000
0.0500

0
5
10
15
20
25
Defaults (%)
LogNormal Distribution: Mean = 5.97; StDev = 2.16
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
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
Broad Geographic Distribution
Through mid2006
Idiosyncratic Causes for Default
What unobserved contingency could upset this pattern?
Threats to Diversification
One candidate was fairly obvious.
$
$
$
$
Falling housing prices would hurt ALL borrowers
Defaults would no longer be statistically independent
12month % change
Strongly Positive: 19952006
10 City Composite U.S. Home Price Index
12month % change
Jan95
S&P/CaseShiller Home Price Indices
Mar 1994
Aug 1990
12month % change
Negative for 31/2 years in early 1990s
10 City Composite U.S. Home Price Index
12month % change
S&P/CaseShiller Home Price Indices
September 2005
Mar 1994
Aug 1990
MonthtoMonth % Change
Peaked in September 2005 : Turned Negative in mid2006
10 City Composite U.S. Home Price Index
12month % change
Monthly % Change (annual rate)
S&P/CaseShiller Home Price Indices
The Lesson
1)Look for significant unrepresented variables.
2)Track these variables carefully as early warning indicators of emerging problems.
The Seeds of SelfDestruction
The huge expansion of subprime mortgage debt set the stage for a more serious crisis when conditions began to worsen.
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
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
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
Complexity
Limited Data
Dark Risk
+
Old Credit Risk Mantra
What is the second means of repayment?
Proposed Capital Markets Mantra
What is the second 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
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)
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
Was this crisis a Black Swan?
?
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 ????
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.
1. Statistical Entropy
2. Structural Imagination
Data
Information
CompoundEconomic Impact
3. SelfReferential Feedback
Recognize that success of an innovation can alter the environment in ways that jeopardize continued success
4. Complexity Dark Risk
5. Second Means of Valuation
?
Limit holdings of assets with no reasonably objective second means of valuation even if they are highly liquid today
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