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Modeling Bank Senior Unsecured Ratings: A Reasoned Structured Approach to Bank Credit Assessment

Author

Listed:
  • Spyros Pagratis

    (Athens University of Economics and Business)

  • Marco Stringa

    (Bank of England)

Abstract

This paper studies the impact of bank-specific financial indicators and macroeconomic variables on bank senior unsecured ratings by Moody’s. Controlling for bank financial characteristics, we find significant evidence of procyclicality in bank ratings stemming from lagged interaction effects between the real output gap and the credit gap. In particular, macroeconomic slowdowns that follow credit booms tend to imply lower ratings. Similarly, when credit expansion above a trend is followed by strong economic performance, bank ratings tend to increase. Bank ratings also appear to correlate positively with the slope of the yield curve and tend to increase with sovereign ratings, market share of lending, and bank size. Given the ongoing debate on the importance, timeliness, and information content of credit ratings in general - and those assigned to banks in particular - the paper addresses a topic that is of great importance to central banks, regulators, and risk managers.

Suggested Citation

  • Spyros Pagratis & Marco Stringa, 2009. "Modeling Bank Senior Unsecured Ratings: A Reasoned Structured Approach to Bank Credit Assessment," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 1-39, June.
  • Handle: RePEc:ijc:ijcjou:y:2009:q:2:a:1
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    2. Aivazian, Sergey & Golovan, Sergey & Karminsky, Alexander & Peresetsky, Anatoly, 2011. "An approach to ratings mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 23(3), pages 13-40.
    3. Dimitras, Augustinos I. & Gaganis, Chrysovalantis & Pasiouras, Fotios, 2018. "Financial reporting standards' change and the efficiency measures of EU banks," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 223-233.
    4. Sujit Kapadia & Matthias Drehmann & John Elliott & Gabriel Sterne, 2012. "Liquidity Risk, Cash Flow Constraints, and Systemic Feedbacks," NBER Chapters, in: Quantifying Systemic Risk, pages 29-61, National Bureau of Economic Research, Inc.
    5. Kokas, Sotirios & Vinogradov, Dmitri & Zachariadis, Marios, 2020. "Which banks smooth and at what price?," Journal of Corporate Finance, Elsevier, vol. 65(C).
    6. Jean-Loup, Soula, 2017. "Measuring heterogeneity in bank liquidity risk: Who are the winners and losers?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 302-313.
    7. Volkova, Olga (Волкова, Ольга) & Lvova, Irina (Львова, Ирина), 2016. "The bank's rating, the rating agencies, Basel II of, financial indicator, the econometric model [Влияние Финансовых Показателей На Международные Рейтинги Российских Банков]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 177-195, February.
    8. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
    9. Drehmann, Mathias & Sorensen, Steffen & Stringa, Marco, 2010. "The integrated impact of credit and interest rate risk on banks: A dynamic framework and stress testing application," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 713-729, April.
    10. Dimitris Anastasiou & Apostolos Katsafados, 2023. "Bank deposits and textual sentiment: When an European Central Bank president's speech is not just a speech," Manchester School, University of Manchester, vol. 91(1), pages 55-87, January.
    11. Meng-Jou Lu & Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle, 2017. "Copula-based factor model for credit risk analysis," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 949-971, November.
    12. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.
    13. David Aikman & Piergiorgio Alessandri & Bruno Eklund & Prasanna Gai & Sujit Kapadia & Elizabeth Martin & Nada Mora & Gabriel Sterne & Matthew Willison, 2011. "Funding Liquidity Risk in a Quantitative Model of Systemic Stability," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 12, pages 371-410, Central Bank of Chile.
    14. Craig Burnside & Mario Cerrato & Zhekai Zhang, 2018. "Foreign exchange order fl ow as a risk factor," Working Papers 2018-03, Business School - Economics, University of Glasgow.
    15. Lu, Meng-Jou & Chen, Cathy Yi-Hsuan & Härdle, Karl Wolfgang & Härdle, 2015. "Copula-Based Factor Model for Credit Risk Analysis," SFB 649 Discussion Papers SFB649DP2015-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    16. Meng-Jou Lu & Cathy Yi-Hsuan Chen & Wolfgang Karl Hardle, 2020. "Copula-Based Factor Model for Credit Risk Analysis," Papers 2009.12092, arXiv.org, revised Oct 2020.
    17. Wolff, Christian & Papanikolaou, Nikolaos I., 2015. "Does the CAMEL bank ratings system follow a procyclical pattern?," CEPR Discussion Papers 10965, C.E.P.R. Discussion Papers.

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    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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