Interest Rate Uncertainty and the Predictability of Bank Revenues

This paper examines the predictive power of interest rate uncertainty over preprovision 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 e_ect 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 crosssectional
information embedded in the panel of BHCs.