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.

• 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

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