FA 636 Advanced Financial Risk Analytics
Course Catalog Description
Introduction
Given the advancement of statistical tools, the course aims to leverage state-of-the-art analytics for financial risk management. The course begins with an overall introduction to risk models such as market, credit, and operational risk. The course then evolves to discuss volatility predictive models using time series analysis and machine learning. It will also discuss multivariate risk systems, copulas, and shrinkage-based techniques for risk assessment. The second half of the course is mostly dedicated to credit risk management. This part of the course will focus on utilizing predictive analytics to develop early warning systems for corporate credit risk. The course will cover recent research articles and statistical computing libraries as part of the learning objectives.
Prerequisites:
- FE 535 Introduction to Financial Risk Management or QF435 Risk Management for Capital Market
- FE 515 Introduction to R or FE 520 Introduction to Python
- FE 590 Statistical Learning in Finance or BIA 656 Statistical Learning and Analytics
| Campus | Fall | Spring | Summer |
|---|---|---|---|
| On Campus | |||
| Web Campus |
Instructors
| Professor | Office | |
|---|---|---|
|
Majeed Simaan
|
msimaan@stevens.edu | |
|
|
More Information
Course Outcomes
After successful completion of this course, students will:
Learning Goals:
- Understand different types of risk such as market, credit, liquidity, and operational
- Apply advanced techniques for univariate and multivariate risk systems
- Apply risk management topics using advanced analytical tools
- Utilize state-of-art data science libraries for risk modeling and optimization
- Build on recent research ideas and data science tools for risk assessment
- Leverage predictive models for market and credit risk management
Lecture Outline
| Topic | Reading | Assignments | |
|---|---|---|---|
| Week 1 | Introduction to Risk Models | Ch. 12 from Jorion | |
| Week 2 | Modeling Risk Factors | Ch. 5 from Jorion Ch. 10 from Hull |
Lab 1: Building Volatility Term Structure |
| Week 3 | Advanced Risk Models: Univariate | Ch. 15 from Jorion Ch. 12 from Hull |
Lab 2: Coherent Risk Measures |
| Week 4 | Volatility Predictive Models: Application of Regime Switching | Ardia, D., Bluteau, K., Boudt, K., & Catania, L. (2018) | Lab 3: The MSGARCH library |
| Week 5 | Volatility Predictive Models: Application of Machine Learning | Carr, P., Wu, L., & Zhang, Z. (2019) | Lab 5: Feature space for volatility models |
| Week 6 | Advanced Risk Models: Multivariate | Ch. 16 from Jorion Ch. 11 from Hull |
Lab 6: The Gaussian Copula |
| Week 7 | Portfolio Risk Management and Shrinkage Techniques for High Dimensional Systems | Ch. 19 from Jorion Ledoit, O., & Wolf, M. (2003) |
Lab 7: Shrinking the Covariance Matrix |
| Week 8 | Midterm exam | Assignment 1 Due | |
| Week 9 | Credit Risk Management I | Ch 19 and 20 from Jorion Ch 18 from Hull |
Lab 8: Early Warning Systems using Financial Ratios |
| Week 10 | Forecasting High-Risk Banks | Gaul, L., Jones, J., & Uysal, P. (2019) | Lab 9: A closer look at CAMELS |
| Week 11 | Credit Risk Management II | Ch. 21 and 23 from Jorion Recommended: Ch 19 from Hull | Lab 10: Merton’s Model I |
| Week 12 | Using the Merton Model for Default Prediction | Afik, Z., Arad, O., & Galil, K. (2016) | Lab 11: Merton’s Model II |
| Week 13 | Operational and Liquidity Risk | Ch. 25 and 26 from Jorion | |
| Week 14 | Liquidity risk of corporate bond returns | Acharya, V. V., Amihud, Y., & Bharath, S. T. (2013) | Lab 12: Measuring Liquidity Risk |
| Week 15 | Final Exam | Assignment 2 Due |