Advanced risk management topics
FIN-474 / 2 credits
Teacher: Acerbi Carlo
Language: English
Remark: MA3 only. Special schedule. See the MFE website: https://go.epfl.ch/fe
Summary
The students learn different financial risk measures and their risk theoretical properties. They learn how to design and implement risk engines, with model estimation, forecast, reporting and validation elements. The students will also overview regulation standards for banks and insurance companies.
Content
· Brief overview of financial regulation
o The Basel Capital Accord, from Basel I to Basel III
o Insurance regulation: Solvency I and II
o Asset management regulation overview
· Introduction to risk measures: examples: Variance, Value at Risk (VaR) and Expected Shortfall (ES)
o Definitions
o Estimation properties: statistical error and bias
· Common practices for portfolio risk modelling
o Montecarlo simulation
o Historical simulation
o Parametric distributions
· Coherent measures of risk
o Axiomatics
o Convex measures of risk
o Proofs of coherence for ES
o Spectral measures of risk; construction and characterization
o Stress Testing
o Deviation measures of risk
o Introduction to liquidity risk
· Risk attribution
o The Euler principle
o The Shapley Value
· Introduction to Portfolio Optimization
o Markowitz mean-variance optimization
o Optimization with coherent measures of risk
· Model validation:
o VaR backtesting methods
o Backtesting General Risk measures
o Elicitability and backtestability
o Ridge backtest for ES and Variance
Keywords
Value at Risk, Expected Shortfall, Coherent Measures of Risk, Stress Testing, Backtesting, Basel Regulation, Solvency Regulation,
Learning Prerequisites
Required courses
tbd
Recommended courses
tbd
Important concepts to start the course
Calculus 1 and 2. Statistics and probability. Portfolio theory.
Learning Outcomes
By the end of the course, the student must be able to:
- Demonstrate ,discuss and illustrate relevant properties of common risk measures
- Model profit-and-loss distribution functions, with analytical methods, Montecarlo or Historical Simulation, as appropriate
- Estimate risk measures from iid data samples
- Quantify risk measures exactly from exact specification of the distribution functions
- Optimize portfolio risk and return using either variance or ES
- Contextualise a risk engine design within financial regulation
- Implement model validation (backtesting) procedures for a risk engine
- Construct risk management dashboards for portfolio hierarchies, with risk attribution features
Transversal skills
- Summarize an article or a technical report.
- Demonstrate the capacity for critical thinking
- Write a scientific or technical report.
Teaching methods
Slide presentations
Coding snippets and examples (python and/or matlab)
Expected student activities
(non marked) episodic programming assignments and challenges
Assessment methods
final project (50/100) plus exam (50/100)
Resources
Bibliography
Embrechts, Frey, McNeil, « Quantitative Risk Management, 2nd ed. », 2015
Ressources en bibliothèque
Notes/Handbook
Professor's Notes (pdf beamer slides)
In the programs
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Advanced risk management topics
- Lecture: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Advanced risk management topics
- Lecture: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Advanced risk management topics
- Lecture: 2 Hour(s) per week x 14 weeks