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Statistical Opacity In The U.S. Banking Industry

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. This property may mask observable drift in risk linkages until aggregate risk levels have risen high enough to trigger new failures, thus suggesting an informational basis for the puzzling recurrence of bank crises.

Suggested Citation

  • Guo Li & Lee Sanning & Sherrill Shaffer, 2009. "Statistical Opacity In The U.S. Banking Industry," CAMA Working Papers 2009-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2009-16
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-02/16_li_sanning_shaffer_2009_revised_080909.pdf
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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