Detecting Bank-Level Liquidity Shifts: Evidence from U.S. Regulatory Data

Ayse Durukan Sonmez (), Jinyan Kuang, Osman Nal and James Marzolf-Miller
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Ayse Durukan Sonmez: Raymond A. Mason School of Business, William and Mary, Williamsburg, VA 23188, USA
Jinyan Kuang: Raymond A. Mason School of Business, William and Mary, Williamsburg, VA 23188, USA
Osman Nal: Raymond A. Mason School of Business, William and Mary, Williamsburg, VA 23188, USA
James Marzolf-Miller: Raymond A. Mason School of Business, William and Mary, Williamsburg, VA 23188, USA

JRFM, 2025, vol. 18, issue 10, 1-20

Abstract: In the wake of the 2008–2009 Global Financial Crisis, the Federal Reserve began paying interest on reserves (IOR) on 1 October 2008—an intervention that, along with others, constituted a regime change for U.S. banks. In this study, we investigate whether banks’ liquidity adjustment was progressive and continuous or abrupt and regime-defining, and how adjustment timing differed across institutions. Using quarterly regulatory call reports from 2002:Q4 to 2015:Q4, we estimate a Gaussian hidden Markov model (HMM) to detect bank-specific regime shifts. We then use the inferred break dates in a regression framework that classifies banks’ liquidity behavior over time. We find a discrete upward shift in liquidity around 2008–2009 with pronounced cross-bank heterogeneity. The patterns persist when we stratify by asset size and remain highly concordant across geographic regions and primary regulators. To illustrate its broader relevance, we extend the framework to the COVID-19 era (2017–2023) for the four largest U.S. banks, showing that it captures comparable regime dynamics across successive phases of quantitative tightening and easing.

Keywords: bank call reports; liquidity ratios; regime changes; Gaussian Hidden Markov Model (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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