| Lecturers: | B. Hassani and H. Pham |
| Period: | Term 1 |
| ECTS: | 3 |
| Schedule: | 3 hours per week |
Objectives
- Alert on the issues around quantitatice measure and risk management faced by financial industries
- Present the main concepts for quantitative analysis of financial risks
- Introduce the statistical aspects of risk measures
- Develop the practical approach for the guidance and the optimisation of risks.
Course 1 (B. Hassani): Introduction : Identification, measure and risk management
1. Global Intro to Risk Management
a. Risk taking (risk vs incidents vs losses)
b. Key classes of risk, their origins and potential impacts
c. Tools and procedures used to measure and manage risk (Quantitative measures, Qualitative assessment, Integrated risk management …)
d. Perception bias
2. Financial Disasters (case studies)
a. "The failure of the gaussian copula" (Title from Wired) - CDOs
b. PPI
c. Rogue trading
d. Systemic issue in 2008
3. Model Risk
a. Sources of Model Risk
b. Mitigation methods and procedures
c. Case studies of Model Risk
Courses 2, 3, 4 (B. Hassani) : Market and Counterparty Risks Measurement and Management
1. Risk Measurement
a. VaR
b. ES
c. Spectral Risk Measure
2. Market Risk
a.. Hedging linear risk
b. Trading and hedging Strategies using options
c. Hedging Vanilla vs Exotic Options
2. Credit Risk (modelisation machine learning)
a. Credit Risks and Credit Derivatives
- Credit Derivatives
- CDS spreads and hazard rates
- Credit VaR
b. Counterparty risk
- OTC derivatives, Counterparty risk intermediation
- Colalteral management
- Credit and debt avlue adjustment
- XVA
Courses 5, 6 et 7 (H. Pham): Basic concepts for quantitative analysis of financial risks
- Valuation of financial risk. Factor risks.
- Risk measures: VaR, Expected Shortfall
- Agreggation of risk and coherent risk measures
- Capital allocation, Euler principle
- Multivariate distribution and dependence
Course 8 (H. Pham): Statistical aspects of risk measures
- Empirical Quantile
- Nonparametric estimation by historical simulation
- Semiparametric estimation by Monte-Carlo
- Parametric estimation by analytical method
- Extreme quantile estimators: Hill, POT
Bibliography
1. P. Artzner, F. Delbaen, J.-M. Eber, and D. Heath, Coherent measures of risk, Mathematical Finance, (1999).
2. P. Embrechts, A. McNeil and R. Frey, Quantititative Risk Management, Princeton University Press, 2006.
3. T. Roncalli, Lecture notes on risk management and financial regulation,
http://www.thierry-roncalli.com/download/Financial_Risk_Management.pdf
4. Addo, Peter Martey, Dominique Guegan, and Bertrand Hassani. "Credit risk analysis using machine and deep learning models." Risks 6.2 (2018): 38.
5. Hassani, Bertrand, and Bertrand K. Hassani. Scenario analysis in risk management. Springer International Publishing Switzerland, 2016.
6. Guégan, Dominique, and Bertrand K. Hassani. Risk measurement. Springer International Publishing, 2019.
7. Hassani, Bertrand K. "Societal bias reinforcement through machine learning: a credit scoring perspective." AI and Ethics 1.3 (2021): 239-247.
8. Carrillo Menéndez, Santiago, and Bertrand Kian Hassani. "Expected Shortfall Reliability—Added Value of Traditional Statistics and Advanced Artificial Intelligence for Market Risk Measurement Purposes." Mathematics 9.17 (2021): 2142.
9. Garcin, Matthieu, Dominique Guégan, and Bertrand Hassani. "A Multivariate Quantile Based on Kendall Ordering: Accepted-September 2021." Revstat-statistical journal (2021).
