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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

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Presentation on theme: "Structured Debt Ratings: Evidence on Conflicts of Interest 1 Matthias Efing University of Geneva and SFI Harald Hau University of Geneva and SFI"— Presentation transcript:

1 Structured Debt Ratings: Evidence on Conflicts of Interest 1 Matthias Efing University of Geneva and SFI Harald Hau University of Geneva and SFI

2 2 Did CRAs grant rating favors to issuers in which they had a large business interest? US Justice Department files lawsuit against S&P S&P memorandum, July 1, 2004: Recognizes role of Client Value Managers in “criteria discussion” consideration of "market perspective" and "rating implications“ polling of "three to five investors in the product" and "an appropriate number of issuers and investment bankers” Empirical evidence for conflict of interest? Is the poor rating performance in 2007–08 the result of market considerations that distort rating processes? Simultaneous downgrades of thousands of structured securities in 2007/08 ABX index of AAA-rated MBS dropped by 70% between Jan ‘07 and Dec ‘08 Research Question

3 3 Literature Theoretical literature: Strong bargaining power of issuers due to “issuer pays model” (Pagano & 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; Opp et al., 2013)

4 4 Literature Empirical literature: Rating favors in corporate bank ratings (Hau, Langfield, & Marquez 2013) Investors require higher yields for MBS sold by large issuers (He, Qian, & Strahan, 2011, 2012) Subjective rating adjustments, and competitive pressure (Griffin & Tang, 2012; Griffin, Nickerson, & Tang, 2013) Launch vs. follow-up ratings (Griffin & Tang 2011) Issuer-pays vs. investor-pays CRA (Cornaggia & Cornaggia, 2013), Revolving Doors on Wall Street (Cornaggia, Cornaggia, & Xian, 2013)

5 5 Data – Structured Debt by Deal Type

6 6 Data – Structured Debt by Origin of Collateral

7 7 Data – Boom-Bust Pattern of Structured Debt

8 8 Collateral Pool AAA AA B Equity 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; decoupling of principle and interest payments; etc.) Varying tranche access to liquidity reserves or debt insurance; etc. Risk allocation to deal tranches intractable for large samples with different asset/collateral types Conduct deal-level analysis

9 9 Deal-Level Measures of Credit Risk Ignore complex intra-deal allocation of credit risk Measure credit risk at deal level: Collateral quality: 90plus delinquency rate 9 months after deal closure Credit enhancement: overcollateralization, debt guarantees, liquidity reserves Deal complexity: number of tranches Moody’s PDS: 620 EMEA & 52 North-American deals with 6,514 tranche ratings.

10 10 Methodology – Rating Implied Spreads

11 11 Estimation of RIS (Rating-Implied Spreads) DCM Analytics and Bloomberg 10,625 floaters issued at par with Euribor/Libor as base rate Rating dummies explain 48% of variation in launch spreads 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

12 12 Methodology – Deal Level Aggregation of RIS

13 13 Aggregation of RIS to Deal Rating-Implied Spread Correlation: 0.55

14 14 Methodology – Determinants of deal ratings

15 15 Hypothesis H1: 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 spreads

16 H1: Evidence from Subordination Levels 16

17 17 H1: Evidence from Deal Rating Implied Spreads


19 19 Hypothesis H2: Rating Favors by Deal Quality and Complexity 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 deals benefit from larger rating favors. (rating precision more expensive; external quality verification more difficult)

20 20 H2: Quantile Regressions

21 21 H2: Quantile Regressions MBS (ABS) account for 57% (43%) of observations with DRIS beyond Q90.

22 … 22 H2: Deal Complexity

23 23 Hypothesis H3: 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)

24 … 24 H3: Conflicts of Interest over the Credit Cycle

25 25 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.

26 … 26 H4: Ratings Shopping over the Credit Cycle

27 27 Reverse Causality CRA errs in Bad Faith: CRA caters rating favors to secure future business. CRA errs in Good Faith: CRA would have to err repeatedly in the same direction across the (many) and potentially heterogeneous deals of an issuer to accumulate a large ASSB. But Good-Faith errors should be independent. Favorable Rating Error (d,a) Rating Mandate (d,a) Repeated Favorable Rating Error Accumulate Large ASSB

28 28 Causality Issues & Structure of Rating Errors

29 … 29 Robustness: CRA fixed effects & interactions

30 … 30 Robustness: Rating Favors across Asset Types

31 Ca. 1.5% of avg. deal unsecuritized Base line regress.: Weight unsec. part of deal with dummy for Unrated Junior Column (1): Weight unsec. deal part with avg. RIS Column (2): Weight unsec. deal part with RIS(Junk) 31 Robustness: Alternative DRIS Models

32 32 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.

33 33 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.

34 34 Main findings and policy implications Statistically and economically large rating favors Rating favors in High Value issuer-agency relationships. Effect difficult to attribute to rating errors made in good faith or to an omitted variable specific to a particular asset class. Reallocation of resources from disadvantaged to large issuers. Competitive distortions, bank concentration, too-big-to-fail banks, break-down of rating-contingent regulation, … ? 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 low quality products causing a quality degradation during the structured debt boom ?


36 36 Structure of Rating Errors Test for error clustering: 1) Use baseline specification to estimate rating errors. 2) Compute error correlation between the deals of a specific issuer for ratings of a specific CRA. 3) (Reject) H0 of a random non-directional Good-Faith error. 4) Do 1) – 4) for High and Low Value relationships separately. 5) Do 1) – 4) computing error correlation only between deals belonging to different asset classes. => Rule out omitted variable bias specific to an asset class.

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