Economic uncertainty, asset prices and bank credit growth: a Bayesian analysis

Abstract

This study aims to examine the impact of economic uncertainty on bank credit growth globally. Specifically, it analyzes how heightened uncertainty impedes credit activities through three primary monetary policy transmission channels: credit supply, credit demand and notably, the asset price channel. The research uses Bayesian regression to analyze a data set encompassing 41 countries from 2001 to 2019. The World Uncertainty Index (WUI) serves as the primary independent variable. The empirical model incorporates proxies for credit supply, credit demand and asset price channels. The Bayesian approach is used for its robustness in handling small samples and its ability to provide intuitive, logically structured posterior probabilities compared to frequentist statistics. Empirical results confirm that economic uncertainty has a significant negative effect on global bank credit growth across three dimensions: (i) credit supply channel, where banks curtail lending or recall loans due to heightened risks, creating systemic ripple effects; (ii) credit demand channel, as firms postpone investments and consumers increase precautionary savings; and (iii) asset price channel, where banks tighten lending conditions despite rising asset values to hedge against instability. Furthermore, the findings suggest that central bank monetary easing may lose effectiveness during periods of extreme uncertainty. To the best of the authors’ knowledge, this study is among the first to apply Bayesian inference to evaluate the nexus between economic uncertainty and credit growth on a global scale. Its primary contribution lies in the comprehensive integration of the asset price channel into the analytical framework – a dimension largely overlooked in previous literature. In addition the adoption of the WUI instead of the traditional economic policy uncertainty index enhances the sample size and ensures higher standardization across diverse economies.