Interest rate uncertainty and the predictability of bank revenues

Abstract

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, 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 noninterest 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.

Forecasting nonperforming loans using machine learning

“…Hence, reducing NPLs is necessary to ensure a sound banking system and promote the overall financial stability of emerging countries. Designing effective policy alternatives to minimize bank NPLs require accurately forecasting future NPLs and properly identifying the factors that lead to NPLs (Cepni, Demirer, et al, 2022; Hajja, 2020; Plakandaras et al, 2015).…”