SCR-Financial-Networks Documentation¶
A Python framework for analyzing financial networks using Spectral Coarse-Graining (SCG), Agent-Based Modeling (ABM), and Graph Neural Networks (GNN).
Overview¶
This project implements a hybrid approach combining Spectral Coarse-Graining with Agent-Based Modeling and temporal Graph Neural Networks to analyze interbank contagion dynamics among European banks.
Key capabilities:
Spectral Coarse-Graining: reduce complex interbank networks while preserving diffusion dynamics
Agent-Based Simulation: model individual bank behaviors under stress scenarios
Temporal GNN: predict spectral properties from graph-structured market data
Risk Metrics: Delta-CoVaR, MES, and SCG-based systemic risk scores
Interactive Dashboard: Dash UI with live GNN training and SCG-vs-Basel comparison
Real Market Data: yfinance stock data and ECB macro indicators
Getting Started