structured debt ratings evidence on conflicts of interest n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Structured Debt Ratings: Evidence on Conflicts of Interest PowerPoint Presentation
Download Presentation
Structured Debt Ratings: Evidence on Conflicts of Interest

Loading in 2 Seconds...

play fullscreen
1 / 29

Structured Debt Ratings: Evidence on Conflicts of Interest - PowerPoint PPT Presentation


  • 70 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Structured Debt Ratings: Evidence on Conflicts of Interest' - efrem


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
structured debt ratings evidence on conflicts of interest
Structured Debt Ratings: Evidence on Conflicts of Interest

Matthias EfingUniversity of Geneva and SFI

Harald HauUniversity of Geneva and SFI

http://www.haraldhau.com

slide2

Research Question

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
literature
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)
slide4

Complexity of Deal Structures

Collateral Pool

  • 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

AAA

AAA

AA

B

Equity

advantages of deal level analysis
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

estimation of ris rating implied spreads
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)
hypothesis
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

h1 evidence from deal rating implied spreads
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)=> DRISreduction of 9 basis points for avg. deal with DRIS = 12 basis points.
hypothesis1
Hypothesis

H2: 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)

h2 quantile regressions1
H2: Quantile Regressions

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

hypothesis2
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)

hypothesis3
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.

robustness alternative dris models
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)
robustness rating favors priced into yield spreads
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.
robustness regression based on aaa subordination
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.
main findings and policy implications
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 pronouncedfor 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.