Course Description and Objective: This course is designed to train the participants in evaluating and managing risks using advanced financial models. Most ofthe course deals with financial risks. It aims to provide the participants a solid background to identify various risks in the financial markets and to acquire the analytical and programming abilities to analyze related risks in different financial settings.
The course covers the following topics: (i) financial time series, volatility models and risk estimation; (ii) modeling risk exposures with value at risk (VaR) and expected shortfall (ES); (iii) multivariate risk models; (iv) introduction to simulation methods (Monte Carlo simulation, historical simulation); (v) backtesting and stress testing.
The material is necessarily analytical and quantitative. Students should have taken Financial Risk Modeling I, and have basic knowledge of undergraduate level calculus and statistics, as well as knowledge offinancial institutions, markets and instruments. Programing skills such as R, Matlab, or Excel are desired, although not required. If you are uncertain about whether you meet the prerequisite requirements, please email me.
Textbooks and Materials
- Required Peter F. Christoffersen, Elements of Financial Risk Management, 2nd edition. Companion site for the book: http://www.elsevierdirect.com/companions/9780123744487
- Recommended John Hull, Risk Management and Financial Institutions, 4th edition.
- Philippe Jorion, Value At Risk: The New Benchmark for Managing Financial Risk, 3rd edition.
- Ruey S. Tsay, An Introduction to Analysis of Financial Data with R The data and code used by the book are available on the author’s website: http://faculty.chicagobooth.edu/ruey.tsay/teaching/introTS/
Course Outline:
The following schedule is tentative and subject to change based on how the class progresses1.
- Topic Risk management and financial returns
- Historical simulation, VaR, Expected Shortfall
- Financial time series analysis
- Financial time series analysis
- Volatility modeling: RiskMetrics and GARCH Volatility modeling: Estimation and Diagnosis
- Midterm Exam
- Nonnormal distribution
- Covariance and correlation models
- Simulating term structure of risk
- Backtesting and stress testing
- Group Presentation
- Final Review
- Final Exam