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Early warning models in real time

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Listed:
  • Jeffery W. Gunther
  • Robert R. Moore

Abstract

Each quarter, banks file a call report, or Report of Condition and Income, containing hundreds of accounting items pertaining to their financial condition. Because call reports are filed quarterly, whereas banks are typically examined about once every twelve to eighteen months, statistical early warning models using call report data potentially provide a more up-to-date picture of a bank's condition than on-site exams alone. Often neglected, however, is the fact that call report data are subject to revision. We find evidence of a strong relationship between on-site exams and call report revisions. In addition, we evaluate a major class of early warning models using both originally published and revised data to assess whether model accuracy in real time is appreciably lower than accuracy measured using revised data. The findings indicate revised data overstate the accuracy of early warning models. The substantial effect of revisions on the accuracy of early warning models, coupled with the finding of a relationship between revisions and exams, points to a substantial auditing role for on-site exams. More generally, our findings point to the need for care in the use of call report data for research in which the real-time flow of financial information is of some concern.

Suggested Citation

  • Jeffery W. Gunther & Robert R. Moore, 2000. "Early warning models in real time," Financial Industry Studies Working Paper 00-01, Federal Reserve Bank of Dallas.
  • Handle: RePEc:fip:feddfi:00-01
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    File URL: http://www.dallasfed.org/assets/documents/banking/fiswp/fiswp0001.pdf
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    References listed on IDEAS

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    1. repec:gam:jsusta:v:10:y:2018:i:4:p:947-:d:137863 is not listed on IDEAS
    2. Douglas Evanoff & Larry Wall, 2001. "Sub-debt Yield Spreads as Bank Risk Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 20(2), pages 121-145, October.
    3. Tjeerd M. Boonman & Jan P. A. M. Jacobs & Gerard H. Kuper & Alberto Romero, 2017. "Early Warning Systems for Currency Crises with Real-Time Data," CIRANO Working Papers 2017s-18, CIRANO.
    4. Casu, Barbara & Clare, Andrew & Saleh, Nashwa, 2011. "Towards a new model for early warning signals for systemic financial fragility and near crises: an application to OECD countries," MPRA Paper 37043, University Library of Munich, Germany.
    5. Balla, Eliana & Rose, Morgan J., 2015. "Loan loss provisions, accounting constraints, and bank ownership structure," Journal of Economics and Business, Elsevier, vol. 78(C), pages 92-117.
    6. Eliana Balla & Morgan J. Rose, 2011. "Loan loss reserves, accounting constraints, and bank ownership structure," Working Paper 11-09, Federal Reserve Bank of Richmond.
    7. John Krainer & Jose A. Lopez, 2008. "Using Securities Market Information for Bank Supervisory Monitoring," International Journal of Central Banking, International Journal of Central Banking, vol. 4(1), pages 125-164, March.
    8. Andrew Stone & Sharon Wardrop, 2002. "Real-time National Accounts Data," RBA Research Discussion Papers rdp2002-05, Reserve Bank of Australia.
    9. Julapa Jagtiani & James Kolari & Catharine Lemieux & G. Hwan Shin, 2003. "Early warning models for bank supervision: Simpler could be better," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q III, pages 49-60.
    10. repec:eee:finsta:v:30:y:2017:i:c:p:181-191 is not listed on IDEAS
    11. John Krainer & Jose A. Lopez, 2003. "How might financial market information be used for supervisory purposes?," Economic Review, Federal Reserve Bank of San Francisco, pages 29-45.
    12. Salvador Marín Hernández & Ester Gras Gil & Marcos Antón Renart, 2011. "Financial information and restructuring of spanish savings banks in a context of crisis. Changes in the regulation; content and evolution of FROB," CIRIEC-España, revista de economía pública, social y cooperativa, CIRIEC-España, issue 73, pages 99-126, October.
    13. Anatoly Peresetsky & Alexandr Karminsky & Sergei Golovan, 2011. "Probability of default models of Russian banks," Economic Change and Restructuring, Springer, vol. 44(4), pages 297-334, November.
    14. R. Alton Gilbert & Andrew P. Meyer & Mark D. Vaughan, 2002. "Could a CAMELS downgrade model improve off-site surveillance?," Review, Federal Reserve Bank of St. Louis, issue Jan., pages 47-63.
    15. Пересецкий А.А., 2007. "Методы Оценки Вероятности Дефолта Банков," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 43(3), июль.

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