Federal Reserve Bank of St. Louis
Factor-based prediction of industry-wide bank stress
This article investigates the use of factor-based methods for predicting industry-wide bank stress. Specifically, using the variables detailed in the Federal Reserve Board of Governors’ bank stress scenarios, the authors construct a small collection of distinct factors. We then investigate the predictive content of these factors for net charge-offs and net interest margins at the bank industry level. The authors find that the factors do have significant predictive content, both in and out of sample, for net interest margins but significantly less predictive content for net charge-offs. Overall, it seems reasonable to conclude that the variables used in the Fed’s bank stress tests are useful for identifying stress at the industry-wide level. The final section offers a simple factor-based analysis of the counterfactual bank stress testing scenarios.
Cite this item
Sean P. Grover & Michael W. McCracken, "Factor-based prediction of industry-wide bank stress"
, Federal Reserve Bank of St. Louis, Review, volume 96, issue 2, pages 173-194, 2014.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This item with handle RePEc:fip:fedlrv:00022
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