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The role of a CAMEL downgrade model in bank surveillance

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  • R. Alton Gilbert
  • Andrew P. Meyer
  • Mark D. Vaughan

Abstract

This article examines the potential contribution to bank supervision of a model designed to predict which banks will have their supervisory ratings downgraded in future periods. Bank supervisors rely on various tools of off-site surveillance to track the condition of banks under their jurisdiction between on-site examinations, including econometric models. One of the models that the Federal Reserve System uses for surveillance was estimated to predict bank failures. Because bank failures have been so rare during the last decade, the coefficients on this model have been \"frozen\" since 1991. Each quarter the surveillance staff at the Board of Governors provide the supervision staff in the Reserve Banks the probabilities of failure by the banks subject to Fed supervision, based on the coefficients of this bank failure model and the latest call report data for each bank. The number of banks downgraded to problem status in recent years has been substantially larger than the number of bank failures. During a period of few bank failures, the relevance of this bank failure model for surveillance depends to some extent on the accuracy of the model in predicting which banks will have their supervisory ratings downgraded to problem status in future periods. This paper compares the ability of two models to predict downgrades of supervisory ratings to problem status: the Board staff model, which was estimated to predict bank failures, and a model estimated to predict downgrades of supervisory ratings. We find that both models do about as well in predicting downgrades of supervisory ratings for the early 1990s. Over time, however, the ability of the downgrade model to predict downgrades improves relative to that of the model estimated to predict failures. This pattern reflects the value of using a model for surveillance that can be re-estimated frequently. We conclude that the downgrade model may prove to be a useful supplement to the Board's model for estimating failures during periods when most banks are healthy, but that the downgrade model should not be considered a replacement for the current surveillance framework.

Suggested Citation

  • R. Alton Gilbert & Andrew P. Meyer & Mark D. Vaughan, 2000. "The role of a CAMEL downgrade model in bank surveillance," Working Papers 2000-021, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:2000-021
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    References listed on IDEAS

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    1. Flannery, Mark J & Houston, Joel F, 1999. "The Value of a Government Monitor for U.S. Banking Firms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(1), pages 14-34, February.
    2. R. Alton Gilbert & Andrew P. Meyer & Mark D. Vaughan, 1999. "The role of supervisory screens and econometric models in off-site surveillance," Review, Federal Reserve Bank of St. Louis, vol. 81(Nov), pages 31-56.
    3. Rebel A. Cole & Jeffery W. Gunther, 1995. "FIMS: a new monitoring system for banking institutions," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Jan, pages 1-15.
    4. Beverly Hirtle & Jose A. Lopez, 1999. "Supervisory information and the frequency of bank examinations," Economic Policy Review, Federal Reserve Bank of New York, vol. 5(Apr), pages 1-20.
    5. R. Alton Gilbert, 1987. "Local economic effects of bank failures," Proceedings 166, Federal Reserve Bank of Chicago.
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    Cited by:

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    2. Anca Pruteanu-Podpiera & Jiří Podpiera, 2008. "The Czech transition banking sector instability: the role of operational cost management," Economic Change and Restructuring, Springer, vol. 41(3), pages 209-219, September.
    3. Douglas D. Evanoff & Larry D. Wall, 2002. "Subordinated debt and prompt corrective regulatory action," FRB Atlanta Working Paper 2002-18, Federal Reserve Bank of Atlanta.
    4. Anca Podpiera & Jiri Podpiera, 2005. "Deteriorating Cost Efficiency in Commercial Banks Signals an Increasing Risk of Failure," Working Papers 2005/06, Czech National Bank.
    5. Donald Morgan & Adam Ashcraft, 2003. "Using Loan Rates to Measure and Regulate Bank Risk: Findings and an Immodest Proposal," Journal of Financial Services Research, Springer;Western Finance Association, vol. 24(2), pages 181-200, October.
    6. Hari Gopal Risal & Sabin Bikram Panta, 2019. "CAMELS-Based Supervision and Risk Management: What Works and What Does Not," FIIB Business Review, , vol. 8(3), pages 194-204, September.
    7. Thomas B. King, 2003. "Discipline and liquidity in the market for federal funds," Supervisory Policy Analysis Working Papers 2003-02, Federal Reserve Bank of St. Louis.
    8. Radu Muntean, 2009. "Early Warning Models for Banking Supervision in Romania," Advances in Economic and Financial Research - DOFIN Working Paper Series 39, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    9. Evanoff, Douglas D. & Wall, Larry D., 2002. "Measures of the riskiness of banking organizations: Subordinated debt yields, risk-based capital, and examination ratings," Journal of Banking & Finance, Elsevier, vol. 26(5), pages 989-1009, May.
    10. Chen, Ting-Hsuan & Lee, Chien-Chiang & Shen, Chung-Hua, 2022. "Liquidity indicators, early warning signals in banks, and financial crises," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    11. Molina, Carlos A., 2002. "Predicting bank failures using a hazard model: the Venezuelan banking crisis," Emerging Markets Review, Elsevier, vol. 3(1), pages 31-50, March.
    12. John S. Jordan & Eric Rosengren, 2002. "Economic cycles and bank health," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    13. Acrey, James Cash & McCumber, William R. & Nguyen, Thu Hien T., 2011. "CEO incentives and bank risk," Journal of Economics and Business, Elsevier, vol. 63(5), pages 456-471, September.

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