  Bank crisis is challenging to define but can be manifested through bank
contagion. This study presents a comprehensive framework grounded in nonlinear
time series analysis to identify potential early warning signals (EWS) for
impending phase transitions in bank systems, with the goal of anticipating
severe bank crisis. In contrast to traditional analyses of exposure networks
using low-frequency data, we argue that studying the dynamic relationships
among bank stocks using high-frequency data offers a more insightful
perspective on changes in the banking system. We construct multiple recurrence
networks (MRNs) based on multidimensional returns of listed banks' stocks in
China, aiming to monitor the nonlinear dynamics of the system through the
corresponding indicators and topological structures. Empirical findings
indicate that key indicators of MRNs, specifically the average mutual
information, provide valuable insights into periods of extreme volatility of
bank system. This paper contributes to the ongoing discourse on early warning
signals for bank instability, highlighting the applicability of predicting
systemic risks in the context of banking networks.
