bank credit ratings: what determines their quality? 1 harald hau university of geneva and sfi sam...

1

Bank Credit Ratings: What Determines their Quality?

Harald HauUniversity of Geneva and SFI

http://www.haraldhau.com

Sam LangfieldUK FSA

David Marques-IbanezEuropean Central Bank

© Harald Hau, University of Geneva and Swiss Finance Institute 2

Why look at bank ratings?

Spectacular rating failures in the 2007/8 crisis expression of a general problem?

Why do bank ratings matter?

Cornerstone of bank regulation (ratings determine Basel credit risk weights for interbank exposure)

Determine financing costs and competitive advantage (resource allocation role across and to bank sector)

Specific rating problems: Opaqueness and interconnectedness Governance: First and second moment of cash flow matter Leverage implies insolvency might always be close


How to measure rating error?

Cardinal Ratings: Translate into absolute measures of default risk

Ordinal Ratings: Imply only relative assessment: banks with high ratings have relatively less default risk

Rating agencies exacerbate confusion about the meaning of their ratings

Dilemma of cardinal ratings: Default risk of all banks explodes in a financial crisis Financial crises are largely unpredictable


Expected Default Frequencies (EFDs)



Any cardinal rating quality measure will be heavily tainted by non-predictability of financial crisis

Bank rating systems do not incorporate systemic risk measures Ordinal rating is what they produce

Quality metric for ratings requires a nonparametric or ordinal approach

Define Ordinary Rating Quality Shortfall (ORQS)



High rating quality: CR rank and EDF rank are strongly related Scattered along the 45 degree line in a CR-rank EDF

rank plot

Low rating quality:

CR rank and EDF rank shows no correlation Uniform distribution in the CR rank – EDF rank plot


Bank rating data

End quarter bank rating data from Moody’s, S&P and Fitch for 1990-2011 on 369 banks headquartered in the U.S and EU15; ignore subsidiary ratings

Uniform rating scale across agencies Further subdivide each grade by rating outlook (if possible)

Use EDF data from Moody’s (measured two years later) EDF calculations are based on the Merton model Drawing on Moody’s data spares us any parameter choices

Use accounting data from Bankscope Obtain 21,131 ORQS measures, 75% fall into the 2000-

2011

© Harald Hau, University of Geneva and Swiss Finance Institute

Credit rating rank and EDF rank

Uniform distribution in the investment grade range (AAA to BBB-)

Correlation only for speculation rating range (BB+ to C)


Credit rating rank and EDF rank

Weak correlation between rating rank and EDF rank also for investment grade range

The ORQS is distance from the 45 degree line


Rank correlations

Investment rang grades (top and middle tier) contain no information about future EDF

Basel II and III grant steep risk weight reduction

Credit

AssessmentAAA to AA- A+ to A- BBB+ to BBB- BB+ to B- Below B- unrated

Risk Weight 20% 50% 100% 100% 150% 100%


Alternative measures: TORQS and DORQS

0.5

11.5

2P

erc

en

t

0 .2 .4 .6 .8 1ORQS

Frequency distribution

Normal approximation

Ordinal Rating Quality Shortfall

0.2

.4.6

.8P

erc

en

t

-1.5 -1 -.5 0 .5TORQS

Frequency distribution

Normal approximation

Box-Cox Transformation of ORQS Use Box-Cox Transform of ORQS to make data more normal: TORQS

Use directional measure of rating quality to capture rating bias:


Hypotheses about Rating Quality

H1: Different in Crisis and after Credit Booms? H2: Different across Agencies? H3: Do Bank Characteristics Matter? H4: Bank Fee Income Creates Rating Bias? H5: Competition Improves Rating Quality? H6: Is Public Disclosure a Remedy for Underrating?


H1: Rating quality in crisis and after credit booms?

Ratings contain slightly more information (in an ordinal sense) during crisis and after strong credit growth (over the last 12 quarters); STD of TORQS = 0.43


H2: Rating quality differs across agencies?

S&P ratings show less positive rating inflation


H3: Do bank characteristics matter for bias?

Focus on large positive rating errors:

Bank Size creates significantly more rating error

STD log assets = 1.8;

STD of DORQS = 0.42;

Higher Loan share (relative to assets) correlates with less error


H4: Bank fee Income creates rating bias?

S&P ratings inflated for banks with high fee incomes


H5: Competition improves rating quality?

Decreasing industry concentration (lower HH index of market concentration) after 2001 decreased ”small” rating errors (log of ORQS)


H6: Is public disclosure a remedy for underrating?

DORQS correlates negatively with Accounting Data Completeness for all quanitiles:

Disclosure weakest for banks with most inflated ratings

Accounting Data Completeness

=

percentage of 40 Bankscope data fields available for each bank


Policy Implications

Bank credit ratings contain very little information; more in the speculative grade range, and more in crisis periods and after credit booms

Basel II and III allow a strong discrimination in risk weights in the investment grade range; this regulatory privilege has no empirical justification; looks more like another example of regulatory capturing

Large banks get significantly better credit ratings [“too big to fail” effect should lower EDF and create opposite effect] Competitive advantage to large banks Capital misallocation


Policy Implications

Conflict of interest between banks and credit rating agencies likely explanation for rating bias in favour of large banks [need agency specific rating business proxy for each bank to nail this]

Competition (lower HH index) correlates positively with rating quality; “small errors” diminish


Policy Implications

Rating Agency Reform: Extending Liability (Dodd-Frank act) seems have

failed (SEC withdrew proposal on ABS) Low quality of bank ratings make it impossible to

create pecuniary incentives for better ratings Rating pay by user unlikely to work if buy-side has

additional agency problems (Calomiris, 2011, Efing 2012)

Corporate Ratings: Bloechlinger, Leippold and Maire (2012) show that better ratings can be constructed based only on public data


Policy Implications

Bank data disclosure is inversely related to the rating bias; underrated banks report considerably more:

Public data disclosure and ratings are substitutes Regulators should see it as their prime objective to

enhance bank disclosure; effective way to decrease the exorbitant power of rating agencies

Europe: Collusion between regulators and banks to minimize bank disclosure in mutual pursuit of non-accountability

bank credit ratings: what determines their quality? 1 harald hau university of geneva and sfi sam...

Documents

rating error

rating outlook

bank ratings

high rating quality

cr rank edf rank plot

bank credit ratings

default risk rating

spectacular rating failures