Modeling Bank Systemic Risk of Emerging Markets under Geopolitical Shocks: Empirical Evidence from BRICS Countries

The growing economic influence of the BRICS nations requires risk models that capture complex, long-term dynamics. This paper introduces the Bank Risk Interlinkage with Dynamic Graph and Event Simulations (BRIDGES) framework, which analyzes systemic risk based on the level of information complexity (zero-order, first-order, and second-order). BRIDGES utilizes the Dynamic Time Warping (DTW) distance to construct a dynamic network for 551 BRICS banks based on their strategic similarity, using zero-order information such as annual balance sheet data from 2008 to 2024. It then employs first-order information, including trends in risk ratios, to detect shifts in banks' behavior. A Temporal Graph Neural Network (TGNN), as the core of BRIDGES, is deployed to learn network evolutions and detect second-order information, such as anomalous changes in the structural relationships of the bank network. To measure the impact of anomalous changes on network stability, BRIDGES performs Agent-Based Model (ABM) simulations to assess the banking system's resilience to internal financial failure and external geopolitical shocks at the individual country level and across BRICS nations. Simulation results show that the failure of the largest institutions causes more systemic damage than the failure of the financially vulnerable or dynamically anomalous ones, driven by powerful panic effects. Compared to this "too big to fail" scenario, a geopolitical shock with correlated country-wide propagation causes more destructive systemic damage, leading to a near-total systemic collapse. It suggests that the primary threats to BRICS financial stability are second-order panic and large-scale geopolitical shocks, which traditional risk analysis models might not detect.