Phase Synchronization in Financial Networks: A Simulation Framework for Systemic Risk Assessment and Macroprudential Policy

This article introduces a novel simulation-based framework for systemic risk assessment in financial networks using phase synchronization theory. To address confidentiality constraints of regulatory micro-data, we generate realistic synthetic banking datasets that replicate key statistical properties, network structures, and temporal dynamics of real financial systems. Financial institutions are modeled as coupled Kuramoto oscillators, where synchronization patterns serve as early indicators of systemic vulnerability. We estimate model parameters using Bayesian MCMC methods and demonstrate that the Phase Synchronization Index (PSI) provides superior early warning signals compared to established benchmarks such as CoVaR and SRISK. The framework also proposes practical macroprudential policy tools, including Synchronization-Adjusted Capital Requirements (SACR) and real-time supervisory dashboards. All code and synthetic data are publicly available to ensure reproducibility and transparency.