Abstract
This study examines multifractal dynamics and efficiency fluctuations in major U.S. banking stock indices across five crisis episodes: the Global Financial Crisis, the Shale Oil Revolution, the COVID-19 pandemic, the Russian-Ukrainian War, and the collapse of Silicon Valley Bank. Using Multifractal Detrended Fluctuation Analysis (MF-DFA), we assess the scaling characteristics of the KBW Nasdaq Regional Banking Index, the Dow Jones U.S. Select Regional Banks Index, the Nasdaq Bank Index and a newly developed U.S. Small Banks Index, denoted as US.SMB. We also apply a Wavelet-based Measure of Correlation (WLMC) to examine time–frequency co-movements between banking indices and monetary, fiscal, financial-regulatory, and sovereign-debt uncertainty. The findings show that all indices exhibit multifractality. The KBW Nasdaq Regional Banking Index and Nasdaq Bank Index show anti-persistent dynamics, whereas the Dow Jones U.S. Select Regional Banks Index and U.S. Small Banks Index exhibit persistent dynamics. Efficiency ratings reveal that the most efficient is the KBW Nasdaq Regional Banking Index, followed by the Nasdaq Bank Index, with patterns of efficiency varying across episodes of crisis, with regional and small banks performing better in severe downturns. Wavelet tests show that monetary policy uncertainty consistently responds negatively, and other types of uncertainty respond both positively and negatively. The research contributes to the literature by providing industry-specific data within the U.S. banking sector, scaling analysis with STL pre-filtering to narrow the scales, creating a new small-bank index, and using WLMC to account for the time–frequency aspects of various sources of uncertainty in the policies.