Banks' Incentives and the Quality of Internal Risk Models

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 AbstractThis paper investigates the incentives for banks to bias their internally generated risk estimates. We are able to estimate bank biases at the credit level by comparing bank-generated risk estimates within loan syndicates. The biases are positively correlated with measures of regulatory capital, even in the presence of bank fixed effects, consistent with an effort by low-capital banks to improve regulatory ratios. At the portfolio level, the difference in borrower probability of default is as large as 100 basis points, which can improve the typical loan portfolio's Tier 1 capital ratio by as much as 33 percent. Congruent with a regulatory motive, the sensitivity to capital is greater for larger, riskier, and more opaque credits. In addition, we find that low-capital banks' risk estimates have less explanatory power than those of high-capital banks with regard to the prices set on loans, indicating that low-capital banks not only have downward-biased risk estimates but that they also incorporate less information.

Forward-looking and Incentive-compatible Operational Risk Capital Framework

“…Mariathasan and Merrouche (2014) show that weakly capitalized banks in countries with weak supervision see large declines in credit risk RWA after model approval, consistent with RWA manipulation. Plosser and Santos (2014) show that low-capital banks underestimate probabilities of default relative to well capitalized firms. Finally, Behn et al (2016) show that internal credit risk models systematically underestimate default rates.…”

Forward-looking and Incentive-compatible Operational Risk Capital Framework

“…Mariathasan and Merrouche (2014) show that weakly capitalized banks in countries with weak supervision see large declines in credit risk RWA after model approval, consistent with RWA manipulation. Plosser and Santos (2014) show that low-capital banks underestimate probabilities of default relative to well capitalized firms. Colliard (2015) develops a framework to study the incentive-compatibility of capital requirements and highlights the challenges in achieving incentive-compatibility when tail risk is significant.…”

“…Robustness to gaming -Basel II internal modeling requirements have been criticized by regulators and academics for offering banks opportunity to game capital requirements (Blum 2008, Tarullo 2008, BCBS 2013, Hoenig and Morris 2013, Mariathasan and Merrouche 2014, Plosser and Santos 2014, Admati 2016). This problem is particularly acute within the AMA framework for two reasons: the lack of modeling standards in the Basel text and the extreme uncertainty associated with estimates of the 99.9 th percentile of the annual operational loss distribution.…”

Risk Management and Regulation

“…At the same time, it has fueled a (mainly) trans-Atlantic discord over the proposed floor, based on supervisory parameters, that seeks to limit the capital relief that can be gained from the application of internal models and hence the variability of risk-weighted assets across firms. Some of the variability can be attributed to differences in national experiences and practices, but there is also some evidence of the active gaming of internal models (Plosser and Santos 2014). While this discord may have political overtones, in essence it represents competing views of regulation emerging post crisis based less on the academic rigor of the under-Current Challenges CHAPTER 7 lying risk measurement methodologies and more on supervisory confidence in their implementability and supervisability.…”