IAM758 FINANCIAL ECONOMETRICS. DATA SCIENCE AND MACHINE LEARNING
| Course Code: |
9700758 |
| METU Credit (Theoretical-Laboratory hours/week): |
3(3-0) |
| ECTS Credit: |
8.0 |
| Department: |
Institute Of Applied Mathematics |
| Language of Instruction: |
English |
| Level of Study: |
Graduate |
| Course Coordinator: |
|
| Offered Semester: |
Fall and Spring Semesters. |
Course Content
This course provides a comprehensive treatment of modern methods for modeling financial and economic data. The first part establishes the foundations of financial time-series analysis. covering stationarity. autoregressive (AR) and moving-average (MA) models. and the stylized facts of financial returns. It then advances to volatility modeling with GARCH-type specifications. realized volatility constructed from high-frequency data. heterogeneous autoregressive (HAR) models. and mixed-data sampling (MIDAS) regressions. The second part introduces machine learning (ML) methods and their applications in finance. including regularized regression. tree-based algorithms. unsupervised learning. and natural language processing (NLP) for text data such as news. regulatory filings. and social media. The course culminates with methods for integrating traditional econometric approaches with machine learning techniques. emphasizing hybrid models and the incorporation of text-derived features into financial forecasting. In addition. the course emphasizes rigorous out-of-sample evaluation. interpretability. and the distinction between causal inference and predictive modeling in financial applications. Practical implementation uses R or Python.
