FA636 Advanced Financial Risk Analytics

Course Catalog Description


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.


  • 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
or instruction permission

Campus Fall Spring Summer
On Campus
Web Campus


Professor Email Office
Majeed Simaan

More Information

Course Outcomes

After successful completion of this course, students will:

Learning Goals:

  1. Understand different types of risk such as market, credit, liquidity, and operational
  2. Apply advanced techniques for univariate and multivariate risk systems
  3. Apply risk management topics using advanced analytical tools
  4. Utilize state-of-art data science libraries for risk modeling and optimization
  5. Build on recent research ideas and data science tools for risk assessment
  6. Leverage predictive models for market and credit risk management

Course Resources


  • Acharya, V. V., Amihud, Y., & Bharath, S. T. (2013). Liquidity risk of corporate bond returns: conditional approach. Journal of Financial Economics, 110(2), 358-386.
  • Afik, Z., Arad, O., & Galil, K. (2016). Using Merton model for default prediction: An empirical assessment of selected alternatives. Journal of Empirical Finance, 35, 43-67.
  • Ardia, D., Bluteau, K., Boudt, K., & Catania, L. (2018). Forecasting risk with Markov-switching GARCH models: A large-scale performance study. International Journal of Forecasting, 34(4), 733-747.
  • Carr, P., Wu, L., & Zhang, Z. (2019). Using Machine Learning to Predict Realized Variance. arXiv preprint arXiv:1909.10035.
  • Gaul, L., Jones, J., & Uysal, P. (2019). Forecasting High-Risk Composite CAMELS Ratings.
  • Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603-621.


Grading Policies

Participation 5% Labs 15% Assignments 20% Midterm exam 30% Final exam 30%

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