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

Battle Count: 69

The paper is primarily focused on systemic risk measurement and financial stability policy rather than trading strategies. However, understanding systemic risk contagion, geopolitical shock propagation, and bank failure cascades is relevant for risk management in quantitative trading portfolios, particularly for emerging market exposure. The TGNN anomaly detection and network centrality measures could inform sector rotation or risk-off signals. The findings about panic-driven contagion vs. structural vulnerability have implications for tail-risk hedging strategies.

The BRIDGES framework integrates four distinct computational components: DTW network construction (O(n^2) pairwise comparisons over 551 banks), EvolveGCN with GRU temporal evolution (trained over 100+ epochs), ABM with Monte Carlo simulations (thousands of runs), and SRISK_CS calculation. Requires expertise in graph neural networks, agent-based modeling, time series analysis, and financial econometrics. The paper states total runtime under 120 minutes, but this depends on hardware. Multiple Python libraries needed for statistical and neural network components.

The paper describes the methodology in detail with mathematical formulations and mentions Python libraries for implementation. However, no code repository is provided. Data comes from BankFocus (commercial database), limiting full reproducibility. The ABM behavioral parameters (alpha, psi) are calibrated via Monte Carlo rather than fixed values, adding some ambiguity. Running time is stated as under 120 minutes.