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      Interest rate uncertainty and the predictability of bank revenues

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      Author(s)
      Cepni, O.
      Demirer, R.
      Gupta, R.
      Sensoy, Ahmet
      Date
      2022-06-24
      Source Title
      Journal of Forecasting
      Print ISSN
      0277-6693
      Electronic ISSN
      1099-131X
      Publisher
      John Wiley & Sons, Ltd
      Volume
      41
      Issue
      8
      Pages
      1559 - 1569
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      This paper examines the predictive power of interest rate uncertainty over pre-provision net revenues (PPNR) in a large panel of bank holding companies (BHC). Utilizing a linear dynamic panel model based on Bayes predictor, we show that supplementing forecasting models with interest rate uncertainty improves the forecasting performance with the augmented model yielding lower forecast errors in comparison to a baseline model which includes unemployment rate, federal funds rate, and spread variables. Further separating PPNRs into two components that reflect net interest and non-interest income, we show that the predictive power of interest rate uncertainty is concentrated on the non-interest component of bank revenues. Finally, examining the point predictions under a severely stressed scenario, we show that the model can successfully predict the negative effect on overall bank revenues with a rise in the non-interest component of income during 2009:Q1. Overall, the findings suggest that stress testing exercises that involve bank revenue models can benefit from the inclusion of interest rate uncertainty and the cross-sectional information embedded in the panel of BHCs.
      Keywords
      Bank stress tests
      Empirical Bayes
      Interest rate uncertainty
      Out-of-sample forecasts
      Permalink
      http://hdl.handle.net/11693/111397
      Published Version (Please cite this version)
      https://doi.org/10.1002/for.2884
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      • Department of Management 639
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