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Structured Debt Ratings:

Evidence on Conflicts of Interest. . 1. Matthias Efing. University of Geneva and SFI. Harald Hau. University of Geneva and SFI. http://www.haraldhau.com. 2. Did CRAs grant rating favors to issuers in which they had a large business interest?.

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Structured Debt Ratings:

Presentation on theme: "Structured Debt Ratings:"— Presentation transcript:


Structured Debt Ratings: Evidence on Conflicts of Interest


Matthias Efing

University of Geneva and SFI

Harald HauUniversity of Geneva and SFIhttp://www.haraldhau.comSlide2

2Did CRAs grant rating favors to issuers in which they had a large business interest?US Justice Department expected to file lawsuit against S&PSpectacular

rating failures during the 2007–08 crisis

2007/2008 crisis triggered by simultaneous downgrades of thousands of structured debt securities (Benmelech & Dlugosz, 2009)

ABX index of AAA-rated MBS dropped by 70% between Jan 2007 and Dec 2008 (Brunnermeier, 2009).Harmful economic implications of rating bias:Undeserved competitive advantages for privileged issuers

Distorted capital allocation Impedes rating-contingent regulation

Research QuestionSlide3

3LiteratureTheoretical literature:

Strong bargaining power of issuers due to “issuer pays model”(

Pagano and Volpin, 2010; White, 2010)Rated firm can “shop for better ratings” (e.g. Skreta & Veldkamp, 2009; Faure-Grimaud et al., 2009)

CRAs might respond to lobbying for rating favors to attract / maintain rating business (Bolton et al., 2012; Mathis et al., 2009) Rating contingent regulation creates incentives to sell regulatory relief in the form of rating favors (Efing, 2012; Harris et al., 2013)Empirical literature:Rating favors in corporate bank ratings (Hau et al. 2013)Investors require higher yields for MBS sold by large issuers

(He et al., 2012)Decline of rating standards during credit boom (Ashcraft et al., 2010)Subjective rating adjustments, rating performance and competitive pressure (Griffin & Tang, 2012; Griffin et al., 2013)Slide4








Complexity of Deal Structures

Credit risk allocated to deal tranches according to seniority

Cash flow cascade further refined

(triggers regulating amortization pro rata vs. in order of seniority; varying tranche access to liquidity reserves or debt insurance; etc.)

Risk allocation to deal tranches intractable for large samples with different asset/collateral types

Deal complexity poses challenge to empirical research on



5Advantages of Deal-Level AnalysisIgnore complex intra-deal allocation of credit riskMeasures of collateral quality and credit enhancement mostly available at deal level:

90plus delinquency rate measured 9 months after deal closure to control for collateral quality

Credit enhancement in the form of overcollaterali-zation, debt guarantees, and liquidity

reservesChallenge of deal-level analysis:Need to summarize tranche ratings to deal-levelSlide6

6Methodology – Rating Implied Spreads


7Methodology – Deal Level Aggregation of RIS


8Methodology – Determinants of deal ratings



Data – Structured Debt by Deal TypeSlide10



– Structured Debt by Origin of CollateralSlide11


Data –

Boom-Bust Pattern of Structured DebtSlide12


Estimation of


(Rating-Implied Spreads)

Data from DCM Analytics and Bloomberg (US and EMEA)

10,625 floating-rate notes


ABS & MBS) issued at par with Euribor/Libor as base rate

Dummies for unrated tranches

Fixed effects and time-interact. for collateral origin, asset type, currency and issuance half-year

Controls for liquidity, maturity and term structure at issuance

Rating Dummies (


) alone explain

48% of variation in launch

spreads (column 1)Slide13

13Aggregation of RIS to Deal Rating-Implied Spread


: 0.55Slide14

14HypothesisH1: Conflicts of Interest and Ratings Inflation

Issuers who generate more rating business (high ASSB) (i) receive better ratings and (ii) benefit from lower rating-implied spreadsSlide15

15H1: Evidence from Subordination LevelsSlide16


H1: Evidence from Deal Rating Implied Spreads

European sample

726 ABS/MBS deals

1,501 deal-CRA pairs

6,638 tranche ratings

Robust std. errors clustered by deal as well as by issuer

Two std. dev. of






reduction of 9 basis points for avg. deal with



12 basis points.Slide17

17HypothesisH2: Rating Favors by Deal Quality and Asset Type

Rating favors are concentrated in those deals for which they are most profitable to issuers and CRAs.Deals of low quality benefit from larger rating favors.

(more profitable than rating favors on already high ratings)More complex ABS

benefit from larger rating favors.(rating precision more expensive; external quality verification more difficult)Slide18

18H2: Quantile RegressionsSlide19

19H2: Quantile Regressions

MBS (ABS) account for

57% (43%) of observations with DRIS

beyond Q90.Slide20


H2: Rating Favors by Asset Type


21HypothesisH3: Conflicts of Interest over the Credit Cycle

Rating favors are more pronounced during credit booms.

(lower default probabilities & reputational costs;

best analysts work for banks rather than for CRAs)Slide22

… 22

H3: Conflicts of Interest over the Credit CycleSlide23

23HypothesisH4: Ratings Shopping over the Credit Cycle

During credit booms risk aversion and

perceived asymmetric information are low. Issuers suppress bad ratings so that deals rated by only one CRA have on average better ratings.

In normal times issuers publish multiple ratings to mitigate adverse selection. Only very risky deals with on average worse ratings are rated by just one CRA.Slide24

… 24

H4: Ratings Shopping over the Credit CycleSlide25

… 25

Robustness: CRA fixed effects & interactionsSlide26

26Robustness: Alternative DRIS Models





deal unsecuritized

Base line regress.:

Weight unsec. part of deal with dummy for

Unrated Junior

Columns (1-2):

Weight unsec. deal part with avg.


Columns (3-4):

Weight unsec. deal part with



Robustness: Rating Favors Priced Into Yield Spreads

Yield spreads might contain a premium for the risk that rating of security is inflated.

Estimate new spread model and control for (log) securitization business shared between CRAs and security issuers.

(coefficient not significant)


all RIS and DRIS and rerun regression for Hypothesis 1.Slide28


Robustness: Regression based on AAA subordination

E.g. Ashcraft et al. (2010), He et al. (2011) use level of AAA subordination to summarize tranche ratings to deal level.Slide29

29Main findings and policy implicationsStatistically and economically large rating favors

Deals receive better credit ratings if CRA has a large business interest

in the deal issuer. Reallocation of resources from disadvantaged to large issuers. Competitive

distortions likely to cause bank concentration and a too big to fail status.Rating favors more pronounced for credit risk lemons

Rating favors twice as large for the 10% of deals with highest rating-implied credit risk. Incentive distortion to supply more and more low quality products to the market causing a quality degradation during the structured debt boom 2004-06.