Interest rate uncertainty and the predictability of bank revenues

Date
2022-06-24
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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
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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.

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Keywords
Bank stress tests, Empirical Bayes, Interest rate uncertainty, Out-of-sample forecasts
Citation
Published Version (Please cite this version)