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Forecasting bank failures: timeliness versus number of failures

Author

Listed:
  • Guo Li
  • Lee Sanning
  • Sherrill Shaffer

Abstract

Motivated by the observation that very few banks fail in normal years, we explore the impact of that pattern on the precision of a standard statistical failure model and discuss implications for regulation and risk management. Out-of-sample forecasting is found to be worse for a model fitted to recent data with few failures than for a model fitted to much older data with more failures.

Suggested Citation

  • Guo Li & Lee Sanning & Sherrill Shaffer, 2011. "Forecasting bank failures: timeliness versus number of failures," Applied Economics Letters, Taylor & Francis Journals, vol. 18(16), pages 1549-1552.
    • Handle: RePEc:taf:apeclt:v:18:y:2011:i:16:p:1549-1552
    DOI: 10.1080/13504851.2010.548777
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    Citations

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    Cited by:

    1. Ghosh, Amit, 2017. "Do bank failures still matter in affecting regional economic activity?," Journal of Economics and Business, Elsevier, vol. 90(C), pages 1-16.
    2. Li, Guo & Shaffer, Sherrill, 2015. "Reciprocal brokered deposits, bank risk, and recent deposit insurance policy," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 366-384.
    3. Yi-Shu Wang & Xue Jiang & Zhen-Jia-Liu, 2016. "Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(9), pages 522-533, September.
    4. Cebula, Richard J. & Xu, Jiay, 2023. "A Brief Survey of Recent Studies of Bank Failures in the U.S," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 76(2), pages 265-274.

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