Project Title:
Advanced Outlier Detection for Yield Curves
Project Objective:
To enhance the accuracy and reliability of yield curve construction by implementing advanced outlier detection techniques using machine learning algorithms. The goal is to identify and mitigate the impact of anomalous data points on the overall yield curve shape.
Technical Approach:
1.Data Acquisition and Preprocessing
- API Integration: Utilized an API to fetch historical yield curve data, including spot rates, maturities, and other relevant information.
- Data Cleaning: Cleaned and preprocessed the data to handle missing values, outliers, and inconsistencies.
2.Feature Engineering
Engineered relevant features for outlier detection, such as:
- Yield curve shape features (level, slope, curvature, convexity)
- Volatility features (historical and implied volatility)
- Time-based features (day of the week, month, year, time of day)
- Market-specific features (economic indicators, market sentiment indices)
3. Anomaly Detection Model Development
- Model Selection: Employed appropriate anomaly detection algorithms, including Isolation Forest, One-Class SVM, or Autoencoders.
- Model Training: Trained the selected models on the preprocessed data to identify anomalous yield curve shapes.
- Hyperparameter Tuning: Optimized model performance through hyperparameter tuning techniques.
4. Model Evaluation and Refinement
- Evaluation Metrics: Assessed model performance using metrics like precision, recall, F1-score, and ROC curve.
- Model Refinement: Iteratively refined the model by adjusting feature engineering, hyperparameter tuning, and algorithm selection.
5. Integration with Yield Curve Construction (Optional)
- API Integration: Integrated the anomaly detection model with the existing yield curve construction pipeline.
- Anomaly Flagging: Flagged anomalous data points for further investigation or correction.
- Model Deployment: Deployed the model to a production environment for real-time anomaly detection.
Impact and Benefits:
- Increased accuracy in yield curve construction, leading to more precise financial analysis.
- Enhanced risk management by identifying and mitigating potential risks associated with anomalous data.
- Informed decision-making through more accurate and reliable yield curve information.
- Automated anomaly detection reducing manual effort and increasing efficiency in the workflow.
Technical Skills Utilized:
- Programming Languages: Python
- Data Engineering: Data extraction, cleaning, transformation, and loading (ETL)
- Machine Learning: Anomaly detection algorithms (Isolation Forest, One-Class SVM, Autoencoders)
- Data Analysis and Visualization: Data exploration, statistical analysis, and data visualization techniques
- API Integration: Interfacing with APIs to fetch and process data
This project significantly improved the quality of financial analysis and decision-making by leveraging advanced machine learning to ensure the robustness of yield curve construction.