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Statistical Opacity In The U.S. Banking Industry

Listed author(s):
  • Guo Li
  • Lee Sanning
  • Sherrill Shaffer

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.

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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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Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2009-16.

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Length: 26 pages
Date of creation: Jun 2009
Handle: RePEc:een:camaaa:2009-16
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Web page: http://cama.crawford.anu.edu.au
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