Structured Debt Ratings: Evidence on Conflicts of Interest

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Structured Debt Ratings: Evidence on Conflicts of Interest

Matthias EfingUniversity of Geneva and SFI

Harald HauUniversity of Geneva and

SFIhttp://www.haraldhau.com

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Did CRAs grant rating favors to issuers in which they had a large business interest?

US Justice Department expected to file lawsuit against S&P Spectacular 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 Question

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Literature

Theoretical 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)

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Collateral Pool

AAA

AAA

AA

BEquity

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 tranche-level

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Advantages of Deal-Level Analysis

Ignore complex intra-deal allocation of credit risk Measures 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 reserves

Challenge of deal-level analysis:Need to summarize tranche ratings to deal-level

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Methodology – Rating Implied Spreads

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Methodology – Deal Level Aggregation of RIS

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Methodology – Determinants of deal ratings

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Data – Structured Debt by Deal Type

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Data – Structured Debt by Origin of Collateral

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Data – Boom-Bust Pattern of Structured Debt

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Estimation of RIS (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 (RIS) alone explain 48% of variation in launch spreads (column 1)

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Aggregation of RIS to Deal Rating-Implied Spread

Correlation: 0.55

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Hypothesis

H1: Conflicts of Interest and Ratings InflationIssuers who generate more rating business (high ASSB) (i) receive better ratings and (ii) benefit from lower rating-implied spreads

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H1: Evidence from Subordination Levels

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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 Log ASSB (2∙1.47) => DRIS reduction of 9 basis points for avg. deal with DRIS = 12 basis points.

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Hypothesis

H2: Rating Favors by Deal Quality and Asset TypeRating 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)

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H2: Quantile Regressions

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H2: Quantile Regressions

MBS (ABS) account for 57% (43%) of observations with DRIS beyond Q90.

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H2: Rating Favors by Asset Type

…

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Hypothesis

H3: Conflicts of Interest over the Credit CycleRating favors are more pronounced during credit booms.

(lower default probabilities & reputational costs; best analysts work for banks rather than for CRAs)

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H3: Conflicts of Interest over the Credit Cycle

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Hypothesis

H4: 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.

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H4: Ratings Shopping over the Credit Cycle

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Robustness: CRA fixed effects & interactions

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Robustness: Alternative DRIS Models

Ca. 1.5% of avg. deal unsecuritized

Base line regress.:Weight unsec. part of deal with dummy for Unrated Junior

Columns (1-2):Weight unsec. deal part with avg. RIS

Columns (3-4):Weight unsec. deal part with RIS(Junk)

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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)

Re-computed all RIS and DRIS and rerun regression for Hypothesis 1.

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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.

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Main findings and policy implications

Statistically 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.