Statistical Opacity In The U.S. Banking Industry
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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