Upon completion of this course, participants will be able to:

• Apply the core principles of **Network Analysis (Graph Theory)** to map the structure of the interbank financial system.
• Construct and validate **interbank exposure matrices** using regulatory reporting and payment system data.
• Utilize key network metrics (e.g., centrality, clustering coefficient, betweenness) to identify **systemically important institutions**.
• Model and simulate **contagion effects** (liquidity and solvency) resulting from a single or multiple bank failure.
• Implement **Stress Testing** methodologies that incorporate the network structure and propagation channels.
• Understand the legal and data governance challenges of sharing and processing confidential interbank exposure data.
• Develop **SupTech** tools and dashboards for real-time visualization and monitoring of interconnectedness risk.
• Analyze the impact of regulatory interventions (e.g., capital surcharges, limits) on the overall network resilience.

• Financial Stability and Macro-Prudential Supervision Analysts at Central Banks.
• Banking Supervisors and Examiners focused on Systemic Risk.
• Heads of Risk Modeling and Stress Testing Departments.
• Quantitative Analysts in Regulatory Agencies (SupTech).
• Economists and Researchers specializing in Financial Networks.
• IT and Data Architects supporting Supervisory Technology initiatives.

• Hands-on Workshops on Network Construction and Metric Calculation (using Python/R and network libraries)
• Simulated Contagion Exercises and Default Cascade Modeling
• Group Activities on Identifying TCTF Institutions in Hypothetical Networks
• Case Studies on Applying Network Analysis to Historical Financial Crises
• Expert Lectures on Data Privacy and Legal Issues in Supervisory Data Sharing
• Individual Assignments on Designing a Network-Based Stress Test Scenario

Unit 1: Fundamentals of Financial Network Analysis

• Introduction to Graph Theory: nodes (banks), edges (exposures), and network structure.
• Types of interbank networks: payment flows, credit lines, derivatives, and funding.
• Data sourcing and cleaning for constructing the **interbank exposure matrix**.
• Limitations of network analysis: the challenge of bilateral exposure data availability.
• Case studies illustrating how network effects amplify systemic crises.

Unit 2: Network Metrics and Systemic Importance

• Calculating key **centrality metrics** (degree, eigenvector, closeness) to quantify interconnectedness.
• Identifying **Too-Connected-To-Fail (TCTF)** institutions based on network position.
• Measures of network density, connectivity, and vulnerability (e.g., core-periphery structure).
• Using network analysis to inform the calibration of capital surcharges and resolution planning.
• The concept of "leverage centrality" and its link to funding markets.

Unit 3: Contagion Modeling and Simulation

• Modeling **solvency contagion** (credit losses) using the DebtRank algorithm and its variants.
• Modeling **liquidity contagion** (fire sales, funding runs) through payment networks.
• Running systemic stress tests by simulating multiple, correlated bank failures.
• Analyzing critical **propagation channels** and identifying network weak points.
• The impact of fire-sales externalities on asset prices and subsequent bank losses.

Unit 4: SupTech Implementation and Data Governance

• Developing in-house **SupTech** tools for network visualization (e.g., Gephi, custom platforms).
• Computational requirements and using parallel processing for large-scale network simulations.
• Data governance, security, and the legal framework for handling confidential exposure data.
• Integrating network analysis outputs into the existing regulatory reporting and risk dashboards.
• Ethical and policy challenges in using network metrics for targeted supervision.

Unit 5: Policy Implications and Oversight

• Using network analysis to design and calibrate **macro-prudential tools** (e.g., LTV limits, capital buffers).
• Analyzing the impact of regulatory changes (e.g., Basel III/IV) on network structure and resilience.
• The role of the central bank as the ultimate source of liquidity and the network anchor.
• Cross-border interconnectedness and international data sharing challenges.
• Translating complex network findings into clear, actionable advice for policy committees.