Comments on: “Did Subjectivity Play a Role in CDO Credit ... · Did Subjectivity Play a Role in CDO Credit Ratings? …but not the way the authors are addressing. Purpose of Securitization

Comments on:“Did Subjectivity Play a Role in CDO

Credit Ratings?”gPrepared for the Federal Reserve Bank of Atlanta Financial Markets Conference

May 11 2010May 11, 2010

by

Joseph R. MasonJoseph R. MasonProfessor and Hermann Moyse, Jr./Louisiana Bankers Association

Endowed Professor of Banking, Louisiana State University and

S i F ll Th Wh S h lSenior Fellow, The Wharton School

Contact [email protected], (202) 683-8909 office. Copyright Joseph R. Mason, 2010. All rights reserved.

Did Subjectivity Play a Role in CDO Credit Ratings?

but not the way the authors are addressing…but not the way the authors are addressing.

Purpose of SecuritizationPurpose of Securitization

P f S iti tiPurpose of Securitization

P f CDOPurpose of CDOs

P f M i ABS CDOPurpose of Mezzanine ABS CDOsABS #1 ABS CDOABS #1AAA/Aaa

AA/Aa2

A/A2

ABS CDOCollateral Pool

ABS #2AAA/Aaa

ABS CDO

Class A

BBB/Baa2Residual

BBB/Baa #2

AA/Aa2

A/A2BBB/Baa2Residual

ABS #3AAA/Aaa

BBB/Baa #1

Class B

Cl C

A/A2 #3AA/Aa2


ABS #4 BBB/Baa #4Class CClass DEquity

AAA/Aaa

AA/Aa2

A/A2BBB/Baa2

ABS #5AAA/Aaa

BBB/Baa #5

BBB/Baa2Residual AA/Aa2


Subprime RMBS Compositions in CDOs Subprime RMBS Compositions in CDOs Grew Quickly over Recent Years

Source: Fitch IBCA (2007)

Ratings Distribution of RMBS in CDO Ratings Distribution of RMBS in CDO Portfolios

Source: Fitch IBCA (2007)

P f CDOPurpose of CDOs

Pre-crisis CDOs Evaluated Pre crisis CDOs Evaluated Rationally

Loans Repurchased and Substituted in Private-label Mortgage Pools

600,000,000

700,000,000

Loans Repurchased and Substituted in Private label Mortgage Pools

400,000,000

500,000,000 Subjectivity?200,000,000

300,000,000

-

100,000,000

5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8 9 9

1/1/

2005

3/1/

2005

5/1/

2005

7/1/

2005

9/1/

2005

11/1

/200

5

1/1/

2006

3/1/

2006

5/1/

2006

7/1/

2006

9/1/

2006

11/1

/200

6

1/1/

2007

3/1/

2007

5/1/

2007

7/1/

2007

9/1/

2007

11/1

/200

7

1/1/

2008

3/1/

2008

5/1/

2008

7/1/

2008

9/1/

2008

11/1

/200

8

1/1/

2009

3/1/

2009

Source: BLIS, Blackbox Logic, June 2009 Remittance Report

CDO StructuresCDO Structures

CDO Hi hli htCDO Highlights• CDO is like a mutual fund that sells debt and • CDO is like a mutual fund that sells debt and

equity– CDOs have multi-layered capital structure with

several debt "tranches"• Early CDOs were backed by portfolios of junk

bondsbonds– Later CDOs emphasized corporate loans or ABS

• Portfolios are actively managed, subject to rulesy g , j• Diversification of the underlying portfolio is the

key to understanding the risk in a CDO

St t Additi l F tStructure – Additional Features• CDO lifecycleCDO lifecycle

– Ramp-up phase– Revolving phase– Amortization phase– Amortization phase

• Waterfall– Pre-2005: mostly sequential

P t 2005 tl t ( ti ith t l )– Post-2005: mostly pro rata (sometimes with toggle)• Collateral quality tests (eligibility)• Performance tests

– Overcollateralization (OC) – par haircuts– Interest coverage (IC)

• Events of Default

B i CDO St tBasic CDO Structure

Oth St tOther StructuresThis is the kind of structure everyone thinks of…

This has three s as ee AAA tranches, but the pricing suggests each has very different risk…

This has two or th AAA three AAA tranches, depending on who you listen to…

Source: Adelson, Mark. CDO/CDS Update 7/2/07, Nomura Fixed Income Research.

HEL ABS D l S C h FlHEL ABS Deal Structure – Cash Flow

• Senior-sub O/C (not prime MBS six pack)Senior sub, O/C (not prime MBS six pack)• Sequential / pro-rata / reverse sequential, with triggers• Importance of Eric Higgins, Joseph Mason, Adi

M d l “Th I f ti C t t f A t B k d Mordel, “The Information Content of Asset Backed Securities Downgrades and the Motivation behind Them” (2010)

Residual B Prin.

M Prin. A Prin.

0 12 24 36 48 60 72 84 96 108 120

Oth St tOther StructuresY Structures

H StructuresH Structures

Combination StructuresCombination Structures

St t Additi l F tStructure – Additional Features• CDO lifecycleCDO lifecycle

– Ramp-up phase– Revolving phase– Amortization phase– Amortization phase

• Waterfall– Pre-2005: mostly sequential

P t 2005 tl t ( ti ith t l )– Post-2005: mostly pro rata (sometimes with toggle)• Collateral quality tests (eligibility)• Performance tests

– Overcollateralization (OC) – par haircuts– Interest coverage (IC)

• Events of Default

E t f D f ltEvents of Default• OC test failures from collateral downgrades• OC test failures from collateral downgrades• Who gets to decide?• What are the choices?• References:

– Jordan P., et al., S&P Offers Guidance on Treatment of CDOs with O/C Events of Default; Two Ratings on W t h N S&P (10/31/07)

Subjectivity?Watch Neg, S&P (10/31/07)

– Kharnak, L., et al., Impact of Subprime Downgrades on OC-Linked Events of Default in CDOs, Moody's (11/1/07)(11/1/07)

– Lioce, S., Understanding the Consequences of ABS CDO Events of Default Triggered by Loss of Overcollateralization, Moody's (1/7/08)

Evolution of CDO ModelingEvolution of CDO Modeling

R ti A A hRating Agency Approaches• Monte Carlo simulation based approaches• Monte Carlo simulation-based approaches• Assumptions

– default frequencies– recovery rates– correlations

• Factor-based correlation modelsFactor based correlation models– Systems for assigning pair-wise correlations

• Software tools:– S&P: CDO Evaluator™ 3.3– Moody's: CDOROM™– Fitch: VECTOR 3.0

Source: Mark Adelson, “Collateralized Debt Obligations and Their Connection to Sub-prime Mortgages,” LexisNexis/Mealeys Conference Presentation, 6 March 2008

S&P M th d l E l ti (1)S&P Methodology Evolution (1)• Grossman R et al "High Yield Cash Flow • Grossman, R., et al., High Yield Cash Flow

Criteria," S&P CreditWeek, p. 19 (16 May 1988)– Initial move into rating CBOs of junk bonds– Rules developed from analysis of hypothetical

historical pool• Global CBO/CLO Criteria (1999)• Global CBO/CLO Criteria (1999)

– No explicit treatment of correlation; instead, focus on "diversification" and "risk concentration"

– Baseline industry concentration of 8%– Excess concentrations addressed by higher default

rate assumptionrate assumption


S&P M th d l E l ti (2)S&P Methodology Evolution (2)• Bergman S "CDO Evaluator Applies Correlation • Bergman, S., CDO Evaluator Applies Correlation

and Monte Carlo Simulation to the Art of Determining Credit Quality" (12 Nov 2001)– Introduction of Monte Carlo simulation approach Introduction of Monte Carlo simulation approach

through CDO Evaluator™– Corporate correlation: 30% intra-industry, 0% inter-

industry• 0% inter-industry assumption sharply criticized

(e.g., Cifuentes A., and N. Chen, "The Young and the Restless: Correlation Drama at the Big Three Rating Agencies," Wachovia Securities (22 Feb 2005))g ( ))

– ABS correlation: 30% intra-sector, 10% inter-sector– Produced lower stressed default rates for ABS than the

earlier Risk Tabulator model


S&P M th d l E l ti (3)S&P Methodology Evolution (3)"C it i F R ti S th ti CDO • "Criteria For Rating Synthetic CDO Transactions" (Sep 2003)– Heavy focus on documentation for synthetics– Heavy focus on documentation for synthetics– Specific treatment of synthetic CDOs of ABS (pp.

57-58)• credit events• settlement mechanisms (i.e., physical and cash)• recoveriesrecoveries


S&P M th d l E l ti (4)S&P Methodology Evolution (4)• Parisi F "Loss Correlations Among U S

Results: Loss Correlations by SF Asset Type Combinations

Parisi, F., Loss Correlations Among U.S. Consumer Assets" (Feb 2004)

(with 95% confidence intervals)

Asset Mfd. Hsg Bank Cards Auto Loans RMBS

0 55Mfd. Hsg. 0.55(0.518, 0.568)

Bank Cards 0.22(0.208, 0.233)

0.17(0.162, 0.179)

Auto Loans 0.37(0.356, 0.390)

0.21(0.201, 0.219)

0.48(0.464, 0.492)

RMBS 0.13 0.07 0.18 0.06RMBS (0.124, 0.133) (0.069, 0.076) (0.172, 0.185) (0.061, 0.064)


S&P M th d l E l ti (5)S&P Methodology Evolution (5)• Bradley E et al "CDO Spotlight: • Bradley, E., et al., "CDO Spotlight:

Synthetic CDO of ABS Documents Evolving Towards a Standard But Nuances Evolving Towards a Standard But Nuances Remain" (26 Apr 2005)– Published a few months before the release of

the first ISDA forms for CDS of ABS– Survey of documentation features

Identified varying practices in credit events – Identified varying practices in credit events and valuation concepts for settlements

– No mention of PAUG structures


S&P M th d l E l ti (6)S&P Methodology Evolution (6)• Gilkes K N Jobst and B Watson "CDO • Gilkes, K., N. Jobst, and B. Watson, CDO

Evaluator Version 3.0: Technical Document" (19 Dec 2005)

R l th ti f 0% i t i d t – Replaces the assumption of 0% inter-industry correlation for corporate bonds

– Produced watchlisting of 35 tranches from 18 synthetic CDO deals

Subjectivity?synthetic CDO deals

• 14 of the 18 deals carried ratings only from S&P• rating shopping issue

Reduced assumed intra industry corporate – Reduced assumed intra-industry corporate correlation to 15%

– Adopts different default rate assumptions for different types of instrumentsdifferent types of instruments


Global Default ProbabilitiesGlobal Default Probabilities

Ratings Default ProbabilitiesCantor and Packer (1996) showed that ratings for SF were more liberal than those for corporates.

AA+ CDO“…in December 2005, when S&P announced the release of version 3.0 of its CDO Evaluator software,

AA- ABSAA+ CDO

the agency published complete tables of default probabilities for ABS/MBS, corporate bonds, and CDOs. In doing so, it created g ,conflicting definitions for its rating symbols depending on the types of instruments to which they apply ”they apply.

Source: Standard & Poor's CDO Evaluator ver. 3.2 as reported in Adelson, Mark, “Bond Rating Confusion,” Nomura Fixed Income Research, June 29, 2006.

Ratings Default Probabilities“…on June 19, [2006] S&P released a new version of its CDO E l ™ Evaluator™ software, including new rating definitions for ABS As part of the ABS. As part of the software release, S&P changed the idealized default probabilities for default probabilities for ABS at different rating levels over different time horizons.”

Source: Standard & Poor's CDO Evaluator ver. 3.2 as reported in Adelson, Mark, “Bond Rating Confusion,” Nomura Fixed Income Research, June 29, 2006.

Ratings Bias v Ratings Quality

Charles W. Calomiris and Joseph R. Mason, “Conflicts of Interest, Low-Quality Ratings, and Meaningful Reform: Evidence

from Debt and Corporate Governance Ratings ” from Debt and Corporate Governance Ratings, (2010)

(policy draft at e21.com)

Eff f C fli f IEffects of Conflicts of Interest• Infl ted r tin s l r l t r• Inflated ratings loosen regulatory

constraints, which could arise either from attempts to maximize portfolio value or from aattempts to maximize portfolio value or from a desire to deceive clients about risk.

• Low-quality ratings arise primarily from theLow quality ratings arise primarily from the desire of investors to deceive clients and thereby invest too much in risky assets on behalf of ytheir clients in order to earn higher asset management fees

Source: Charles W. Calomiris and Joseph R. Mason, “Conflicts of Interest, Low-Quality Ratings, and Meaningful Reform: Evidence from Debt and Corporate Governance Ratings,” (2010)

Potential Sources of Conflict of Potential Sources of Conflict of Interest

Rating Agencies Institutional Investors

Ultimate Investors/Clients

•Preference for meaningful signal

•Preference for meaningful (?)

•Preference to produce meaningful signal

of investment quality.•Preference for

meaningful (?) signal of investment quality.

produce meaningful (?) signal of investment

risk-adjusted return

Regulatory arbitrage

Increased fees

quality.Issuer paysResponds to

institutional institutional investor demand

Source: Charles W. Calomiris and Joseph R. Mason, “Conflicts of Interest, Low-Quality Ratings, and Meaningful Reform: Evidence from Debt and Corporate Governance Ratings,” (2010)

Potential Sources of Conflict of Potential Sources of Conflict of Interest

We conclude that for both debt and corporate We conclude that, for both debt and corporate governance ratings, the low quality of ratings is largely

the result of the principal-agent conflict between institutional investors and ultimate investors.

Rating Agencies Institutional Investors

Ultimate Investors/ClientsSubjectivity?

•Potentially •Potentially •Potentially motivated by:

Plausible deniability

•Potentially motivated by

Shirking Institutional

Regulatory arbitrage

Increased fees

investor demand.

Source: Charles W. Calomiris and Joseph R. Mason, (2010)

ConclusionsConclusions

Conclusions and Conclusions and Recommendations

R• Recourse• Structure Details• Modeling Techniques• Default Probability BiasDefault Probability Bias

Subjectivity?Subjectivity?

Comments on:“Did Subjectivity Play a Role in CDO

Credit Ratings?”gPrepared for the Federal Reserve Bank of Atlanta Financial Markets Conference

May 11, 2010y

by

Joseph R. MasonJoseph R. MasonProfessor and Hermann Moyse, Jr./Louisiana Bankers Association

Endowed Professor of Banking, Louisiana State University and

S i F ll Th Wh S h lSenior Fellow, The Wharton School

Contact [email protected], (202) 683-8909 office. Copyright Joseph R. Mason, 2010. All rights reserved.

AppendixAppendix

A l C h CDO IAnnual Cash CDO Issuance500

400

450

500

250

300

350

150

200

0

50

100

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

Source: Lucas, Goodman, and Fabozzi (2006)

Moody's Methodology Evolution Moody s Methodology Evolution (1)

• Lucas D N Kirnon and L Moses "Rating Cash • Lucas, D., N. Kirnon, and L. Moses, Rating Cash Flow Transactions Backed by Corporate Debt" (Mar 1991)– Original methodologyOriginal methodology– Based on "WARF" and "Diversity Score"– Credit enhancement from tables– 32 industry classifications– 32-industry classifications

• Backman, A. and G. O'Connor, "Rating Cash Flow Transactions Backed by Corporate Debt, 1995 Update" (7 Apr 1995)Update (7 Apr 1995)– Essentially the same methodology– Addressed additional situations (estimated and implied

ratings)ratings)



Moody's Diversity Scoring

Firms in Same Industry Diversity Score

Moody's Rating Factors

Rating Factor Rating Factor

1 1.00

2 1.50

3 2.00

Aaa 1 Baa3 610

Aa1 10 Ba1 940

Aa2 20 Ba2 1,3504 2.33

5 2.67

6 3.00

7 3 25

Aa2 20 Ba2 1,350

Aa3 40 Ba3 1,780

A1 70 B1 2,2207 3.25

8 3.50

9 3.75

10 4 00

A2 120 B2 2,720

A3 180 B3 3,490

Baa1 260 Caa 6,50010 4.00

>10 Case-by-case

,

Baa2 360 Ca 10,000

Source: Backman, A. and G. O'Connor, "Rating Cash Flow Trans-actions Backed by Corporate Debt, 1995 Update" (7 Apr 1995)

Moody's Methodology Evolution (3)Moody's Methodology Evolution (3)• Cifuentes A. and G. O'Connor, "The Binomial Expansion

Method Applied to CBO/CLO Analysis" (13 Dec 1996)– Introduced "binomial expansion technique"– assumptions: default probability, diversity score, recovery rate– Formula for calculation of expected loss

D! p = g b bilit f d f ltjDjj pp

jDjDP −−−

= )1()!(!

! p = average probability of default

D = diversity score

j = no. of defaults in jth scenario

∑=

=D

jjjEP

1Loss Expected

j no. of defaults in j scenario

Pj= probability of scenario j

Ej= expected loss in scenario j=j 1



• Cifuentes A and C Wilcox "The Double • Cifuentes, A. and C. Wilcox, The Double Binomial Method and Its Application to a Special Case of CBO Structures" (20 Mar 1998)

Ad t ti f BET t i l – Adaptation of BET to special cases– DBM offers better view of certain pools such as 80%

emerging market debt combined with 20% U.S. high yield bank loanshigh yield bank loans

– Difference between BET and DBM is important only for low diversity pools (D<10) composed of two distinct asset groups (barbelled assets)distinct asset groups (barbelled assets)

– Example: two uncorrelated groups of assets with markedly different average properties



• Gluck J and H Remeza "Moody's Approach to Rating Gluck, J. and H. Remeza, Moody s Approach to Rating Multisector CDOs" (15 Sep 2000)– Introduced "alternative diversity score methodology" for

assets with correlated default risk

⎟⎠

⎞⎜⎝

⎛⎟⎠

⎞⎜⎝

⎛ ∑∑==

n

iii

n

iii n

FqFpD ifd11

– Correlation parameters disseminated confidentially

ρρρρ

=−+

=⎠⎝⎠⎝= ΣΣ==

ijjijjiiij

ii

nFFqpqpD if

)1(1and11

– Correlation parameters disseminated confidentially– Covered structured finance assets in addition to corporates– Addressed geographic, servicer, and vintage concentrations



• Tolk J "Understanding the Risks in Credit Default • Tolk, J., Understanding the Risks in Credit Default Swaps" (16 Mar 2001)– Initial focus on ISDA credit events for corporates– Comparison to Moody's default definition– Comparison to Moody s default definition

• issue of "soft" (non-default) credit events– Valuation and settlement– Brief mention of non-corporate reference creditsBrief mention of non-corporate reference credits

• Yoshizawa, Y., "Moody's Approach to Rating Synthetic CDOs" (29 July 2003)

Focus on multiple binomial method– Focus on multiple binomial method– Par per diversity test– Soft credit event update (stress default probabilities 5%

to 12 5%)to 12.5%)



• Witt G "Moody's Correlated Binomial Default ρWitt, G., Moody s Correlated Binomial Default Distribution" (10 Aug 2004)– Alternative to diversity score for capturing correlation– Assumes single default correlation between all asset pairs

ρg

– Also assumes default prob., recovery rate, & no. of assets– Closed form solution but computationally hard

• Fu, Y., et al., "Moody's Revisits Its Assumptions Regarding C D f l ( d A ) C l i f CDO (30 Corporate Default (and Asset) Correlations for CDOs (30 Nov 2004)– Inter-industry asset correlation 3%

Intra industry asset correlation 15%– Intra-industry asset correlation 15%• telecom and utilities 20%• chemicals, electronics, retail, textiles 10%



• Toutain O et al "Moody's Revisits Its Assumptions Toutain, O., et al., Moody s Revisits Its Assumptions Regarding Structured Finance Default (and Asset) Correlations for CDOs (27 July 2005)– "Tree" approach to sector risk: global, meta, broad, narrow– Add-ons for regional, vintage, and "key agent" effects– Complicated…

• Xie, M. and G. Witt, "Moody's Modeling Approach to Rating St t d Fi C h Fl CDO T ti " (26 S Structured Finance Cash Flow CDO Transactions" (26 Sep 2005)– Use CBM with single correlation parameter from CDOROM™– Assumed pair-wise correlations generate the single Assumed pair wise correlations generate the single

correlation parameter within CDOROM™• simulates losses• applies moment matching scheme to match skew of CBM loss

distribution to that of CDOROM™ simulated loss distributiondistribution to that of CDOROM simulated loss distribution



• Kim T "Moodys Initial Views on the Dealer Form of Kim, T., Moodys Initial Views on the Dealer Form of Confirmation for Pay-As-You-Go Derivative Transactions" (21 Jun 2006)

• Bharwani, P., "Moody's Approach to Rating Collateralized Debt Obligations with Pay-As-You-Go Credit Default Swaps" (13 Nov 2006)– Objects to appraisal reduction as writedown

Strongly objects to implied writedowns but allows as floating – Strongly objects to implied writedowns but allows as floating payment event if certain conditions satisfied

– Rating downgrade to Caa OK w/ physical settlement only• Ca cash settle OK after six monts• C cash settle OK immediately

– Counterparty risks– Amendments
