Bank ratings: what determines their quality?

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may ABSTRACTThis paper examines the quality of credit ratings assigned to banks in Europe and the United States by the three largest rating agencies over the past two decades. We interpret credit ratings as relative assessments of creditworthiness, and define a new ordinal metric of rating error based on banks' expected default frequencies. Our results suggest that rating agencies assign more positive ratings to large banks and to those institutions more likely to provide the rating agency with additional securities rating business (as indicated by private structured credit origination activity). These competitive distortions are economically significant and help perpetuate the existence of 'too-big-to-fail' banks. We also show that, overall, differential risk weights recommended by the Basel accords for investment grade banks bear no significant relationship to empirical default probabilities. Non-technical summaryCredit ratings play a key role in the financial system, but the determinants of their quality are poorly understood. This paper focuses on the information content of bank credit ratings, which affect the costs of the annual issuance of more than US$600 billion in unsecured bank debt in Europe alone.Our analysis provides the most comprehensive analysis of bank rating quality so far, based on approximately 39,000 quarterly bank ratings over 1990-2011 from Standard and Poor's, Moody's and Fitch. We deploy a new method for evaluating rating quality, which interprets bank credit ratings in a strictly ordinal manner: that is, as relative measures of credit risk. Banks are ranked firstly by their credit rating and secondly by their expected default frequency two years later. The difference between these two ranks is defined as the Ordinal Rating Quality Shortfall (ORQS), which provides a good measure of relative rating error, since it does not require measurements of bank risk to be correct in absolute terms. Moreover, using expected default frequencies directly from Moody's KMV database precludes arbitrariness in modelling choices.Four key findings emerge. First, ordinal rating quality is countercyclical. The (ordinal) information content of credit ratings is higher during banking crises. This finding confirms the prediction of some of the theoretical literature, which posits that the net benefits to rating agencies of providing good quality ratings are lower during peaks in the business cycle.Second, bank rating...

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“…18 Hypothesis 2. The informativeness of credit rating changes is high in a crisis period, and is 16 A similar argument is provided as well by Hau, Langfield, and Marques-Ibanez (2013) with respect to big banks. 17 This is also consistent with the model of Skreta and Veldkamp (2009), who motivate rating inflation via rating shopping.…”

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“…It is always difficult to decide how much any upturn reflects enduring changes and how much simply a cyclical improvement that will disappear in due course. Natural optimism might suggest an upward bias but Hau et al (2012) suggest that rating agencies have a relative bias as well. The same conflicts of interest that may contribute to this bias do not apply to the authorities.…”

“…l a r c i e r may be important. Hau, Langfield, and Marqués-Ibañez (2012) suggest that rating agencies systematically underestimate the relative risks of larger banks, in part because they feel they are too-big-to-fail, but also because those banks can exert disproportionate influence on the raters. While we use what are apparently Standardized beta coefficients; t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 l a r c i e r unbiased measures of the state of the bank, it is possible that the authorities may not react unambiguously to the sign of fragility irrespective of size of the bank.…”

“…However, there is clear empirical evidence from the global financial crisis (GFC) and earlier that risk-weighted capital buffers were not good predictors in practice. Hau et al (2012) argue that "Basel risk-weights applied to claims on institutions do not reflect underlying relative risk." Northern Rock for example was fully compliant with risk-weighted measures shortly before its failure (Mayes and Wood, 2009).…”

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