Transcript Title Is Times New Roman 32 Pt., Line Spacing .9 Lines
Are there Central Problems in Credit?
Peter Cotton, Sep 2006
With thanks to Sivan Mahadevan, Pinar Onur, Peter Polanskyji, Andrew Sheets who produced many of the slides herein
Why did Black commit suicide?
Dottore Turati Alexander Luzhin (Black to Move)
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Table of Contents
Section 1: Synthetic CDOs Section 2: Trading Correlation Section 3: Problems Section 4: Open Problems 3
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Section 1: Collateralized Debt Obligations
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CDOs are Companies
Priority of Claim on
Assets
Rises with Seniority Source: Morgan Stanley Priority of Claim on
Collateral
Rises with Seniority 5
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A Typical CDO Capital Structure
Losses from Reference Portfolio are borne by tranches in reverse seniority order Super Senior Tranche (70%) AAA Tranche (7.5%) AA Tranche (3%) A Tranche (2.5%) BBB Tranche (4%) BB Tranche (3%) Equity Tranche (10%) 30 % 22.5% 19.5% % 17 % 13 % 10 % 0 % Source: Morgan Stanley
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Meet the Tranches…
Copyright 2006 Morgan Stanley 7
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Growth in credit markets
Global Total Synthetic CDO Issuance (USD Billions)
350 300 250 200 150 100 50 0 2002 2003 funded 2004 2005 unfunded Q1 2006 Source: CreditFlux
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Other ways to invest
Tranched Credit Risk Small Baskets Credit Options Basis Trades Indexation (TRAC-X SM Dow Jones CDX) Curve Trades Capital Structure Trades Single Name CDS Please see additional important disclosures at the end of this report.
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Section 2: Trading Correlation
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Corporate defaults are not independent
(If history is to be believed)
BBs – 1 Yr and 5 Yr by Cohort
30% 25% 20% 15% 10% 5% 0% 1970 1975
1 Year
5 Year 1980 1985 Cohort 1990 1995 2000 2005 Source: Morgan Stanley, Moody’s
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Corporate defaults are not independent
(If the market is to be believed)
Series 5 Yr
3-7% 7-10% 10-15%
7 Yr
3-7% 7-10% 10-15%
10 Yr
7-10% 10-15%
Zero Correlation CDX 6
6 0 0 13 0 0 24 0 0
CDX 4 Market CDX 5 CDX 6
42 8 4 51 13 6 70 16 8 138 0 0 176 0 0 37 0 53 0 226 1 0 75 1 155 32 14 93 46 171 37 16 99 47 196 44 19 107 52
CDX 4 Difference CDX 5 CDX 6
36 8 4 38 13 6 46 16 8 17 32 14
-5
37 16
-30
43 19 56 46 46 47 32 51 Source: Morgan Stanley
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Correlation and Minefields
Low Correlation High Correlation 13
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Correlation Intuition
Subordinate tranches
Spread
decreases
correlation rises as
Senior tranches
Spread
increases
correlation rises as
Fundamental Correlation Relationships
bp 400 300 200 100 0 0% 20% 40% 60% 80% 100% 14 Senior Tranches Source: Morgan Stanley Subordinate Tranches
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Correlation example: Pensions and Labor Costs
Issues such as pension and labor costs that have plagued the airline industry may spread to other sectors
Similar issues could prove to be a driver in determining default correlation across sectors
This list highlights companies where significant potential exists for a high realized default correlation
Not Only One Sector – Unfunded Pension Liabilities Relative to Equity Market Capitalization Issuer
Delta Air Lines Inc 1 Northwest Airlines Corp 1 Exide Technologies Delphi Corp Continental Airlines Inc AK Steel Holding Corp AMR Corp Goodyear Tire & Rubber Co Hayes Lemmerz Int’l Inc Dura Automotive Systems Inc Visteon Corp Ford Motor Co Austral Pacific Energy Ltd General Motors Corp ArvinMeritor Inc Unisys Corp Navistar International Corp Smurfit-Stone Container Corp Abitibi-Consolidated Inc Tenneco Automotive Inc Bowater Inc PolyOne Corp Dana Corp Hercules Inc TRW Automotive Holdings Corp Terra Industries Inc Timken Co Alcan Inc
Ticker
DAL NWAC XIDE DPH CAL AKS AMR GT HAYZ DRRA VC F AEN GM ARM UIS NAV SSCC ABY TEN BOW POL DCN HPC TRW TRA TKR AL
Industry Group
Airlines Airlines Auto Parts & Equipment Auto Parts & Equipment Airlines Iron/Steel Airlines Auto Parts & Equipment Auto Parts & Equipment Auto Parts & Equipment Auto Parts & Equipment Auto Manufacturers Oil & Gas Auto Manufacturers Auto Parts & Equipment Computers Auto Manufacturers Forest Products & Paper Forest Products & Paper Auto Parts & Equipment Forest Products & Paper Chemicals Auto Parts & Equipment Chemicals Auto Parts & Equipment Chemicals Metal Fabricate/Hardware Mining
Funding Gap/ Mkt Cap
1235% 875% 266% 256% 215% 147% 145% 112% 102% 81% 73% 68% 62% 43% 40% 36% 34% 33% 33% 32% 29% 27% 25% 22% 22% 22% 29% 24% 1 Market Cap based on the average of the 6 months prior to the bankruptcy filing Source: Morgan Stanley
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Section 3: Problems
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Real-World Correlation?
World A another – likelihood of 100 defaults are equally related to one - World B the rest – the default propensity of three companies are highly correlated to each other but not to
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Correlation – Details Matter WORLD A Credit
1 2 3 4 5 … 100
1 2
50%
5 3
50%
4
50% 50% 50% 50% 50% 50% 50% 50% 50% … 50% 50% 50% … 50% 50% 50% 50% … 50% 50% 50% 50% 50% … 50% … 50%
… 100
… 50% … 50% … 50% … 50% … 50%
Average
50%
WORLD B Credit
1 2 3 4 … 100
1 2
90%
5 3
90%
4
10% 90% 10% 10% 10% 90% 90% 10% … 90% 10% … 10% 10% 10% 10% 10% … 50% … 50% 50% 10% 50%
… 100
… 10% … 10% … 10% … 50%
Average
48% Source: Morgan Stanley
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Dynamics of a Static Model
TRACERS spread scenario with five defaults in the first two years
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Modeling correlation in spreads (intensity models)
(For illustration, we identify spreads with hazard rates)
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Problem 1: Model Construction
Build a
dynamic
credit model for hundreds of assets:
Consistent across sub-portfolios Internally consistent and computable dynamics Tractable pricing and estimation Can be calibrated to CDS, index, options, tranches
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Problem 2: Estimation
c.f. Specification analysis of affine term structure models • • • • Dai & Singleton Duffie & Singleton Gallant & Tauchen Hansen An efficiently simulated can be
efficiently, consistently
estimated… eventually Advantages • • • Elegant Can include arbitrarily complex products No likelihood function required (5000 iterations)(1000 steps)(10,000 paths)(1 sec) = 2180 years 21
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Problem 3: Deal Dynamics
How will structured deals & companies evolve?
- Ratings stability - Contingent payments - Corporate events - Optimal management How will desks optimize their correlation trading operations? - Monetization strategies - Contingent hedging costs - Risk management - Capital allocation 22
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Board Vision
Dottore Turati Alexander Luzhin (Black to Move and Win)
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Raising the bar
Solve hard modeling & numerical problems Leverage emerging technologies Leverage advances in other fields --> Enhance and
challenge
human decision making
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