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

• Identify and evaluate the most relevant **real-time interbank market indicators** for financial stress and systemic risk.
• Apply advanced econometric methods (e.g., signal extraction, dynamic factor models) to build robust **Early Warning System (EWS)** models.
• Utilize Machine Learning techniques (e.g., classification, anomaly detection) to enhance the predictive power and accuracy of EWS.
• Design a **Real-Time Data Architecture** and pipeline for the high-velocity ingestion and processing of interbank data.
• Establish clear **alert mechanisms** and policy protocols for responding to EWS signals (e.g., escalation, investigation).
• Analyze the legal and governance challenges of using confidential, high-frequency interbank data for surveillance.
• Evaluate the trade-off between EWS sensitivity (catching all events) and specificity (minimizing false alarms).
• Integrate network-based contagion metrics into the EWS framework for identifying interconnectedness risk.

• Financial Stability and Macro-Prudential Surveillance Analysts.
• Economists and Quantitative Researchers in Central Banks and Regulatory Agencies.
• Heads of Payments Systems and Interbank Market Operations.
• Risk Management and Supervisory Technology (SupTech) Specialists.
• IT Architects designing Data Analytics Platforms.
• Senior Policy Makers involved in Crisis Prevention.

• Hands-on Python/R Workshops on Implementing Probit/Logit EWS Models and ML Classifiers
• Simulated Real-Time Alert Triage and Escalation Exercises
• Group Activities on Defining a Policy Response Protocol for a Hypothetical EWS Alert
• Case Studies on Historical EWS Performance and Missed Events
• Expert Lectures on SupTech Architecture for High-Frequency Data
• Individual Assignments on EWS Model Calibration and Threshold Setting

Unit 1: The EWS Rationale and Data Landscape

• Defining the purpose and historical performance of Early Warning Systems.
• Key high-frequency data sources: interbank lending rates, collateral movements, payment queue times, FX swap markets.
• The concept of "flight to quality" and its manifestation in real-time data.
• Data synchronization, cleaning, and managing the high-frequency nature of interbank data.
• Case studies on the real-time indicators that signaled the 2008 and 2020 crises.

Unit 2: Econometric and Statistical Modeling for EWS

• Applying Probit/Logit models and Signal Extraction methods for binary crisis prediction.
• Using composite indices and dynamic factor models to distill complex stress signals into a single indicator.
• Calibration and backtesting of econometric EWS models against historical financial stability events.
• Techniques for managing the **rare event** problem (low frequency of crises) in EWS modeling.
• Setting dynamic thresholds for alert generation and signal reliability.

Unit 3: Machine Learning for Predictive EWS

• Utilizing Classification algorithms (e.g., Random Forests, Gradient Boosting) for predicting imminent stress.
• Applying **Anomaly Detection** (e.g., Isolation Forest) to high-frequency trading and flow data.
• Integrating textual data (news, sentiment) via NLP into the EWS framework.
• Deep learning models (e.g., LSTMs) for forecasting key market variables under stress.
• Addressing model instability and drift in real-time ML-based EWS.

Unit 4: Architecture, Policy, and Governance

• Designing the **Real-Time Data Pipeline** for EWS (data ingestion, processing, feature store).
• Establishing clear **Action Protocols** and escalation processes for EWS alerts.
• The role of the EWS in informing macro-prudential committee decisions and policy actions.
• Data governance, security, and the legal framework for using confidential interbank data.
• Translating technical EWS outputs into clear, concise policy briefings.

Unit 5: EWS Validation and Future Trends

• Validation metrics for EWS: Type I (false positive) vs. Type II (missed event) errors and the receiver operating characteristic (ROC) curve.
• Integrating network analysis and contagion metrics into the EWS platform.
• Future of EWS: using Central Bank Digital Currency (CBDC) data for enhanced surveillance.
• Continuous monitoring and retraining strategies for EWS models.
• Benchmarking EWS performance against international peer systems.