At the end of the course, the student will possess an adequate mathematical terminology, learned the main quantitative and computational tools to be able to work in the risk management unit of a bank or insurance company.
Expected learning outcomes
At the end of the course, the student will know the basic elements of the Basel and Solvency regulatory frameworks for banks and insurance companies; will possess an adequate mathematical terminology and learned the main quantitative tools related to the study of risk variables and measures in quantitative risk management; will be able to recognize statistically the presence of an elliptical or heavy-tailed distribution and determine its influence on a risk portfolio; will be able to code a software for the computation of the capital reserve needed by a financial institution to comply with the above regulatory frameworks; will be aware of the basic quantitative tools to perform the stochastic aggregation of various typologies of risks.
Lesson period: Second trimester
(In case of multiple editions, please check the period, as it may vary)
- Prerequisites - Overview of Basel and Solvency regulatory frameworks. - Basic Concept in Risk Management: Risk Measures (VaR and ES). - Light tailed versus Heavy-tailed distributions. Regularly varying distributions. The Mean excess function. - EVT: the POT method. - Risk Aggregation via copulas - Elliptical Distributions and Coherent Risk Measures. - Model Uncertainty. - Standard methods for Market Risk - Model Uncertainty and the Rearrangement Algorithm - Operational Risk: some case studies.
Prerequisites for admission
Theory of Integration and elementary probability.
Lectures with exercises (also group assignments) and programming in R
Quantitative Risk Management: Concepts, Techniques and Tools. Revised Edition. AJ McNeil, R Frey and P Embrechts Princeton University Press, Princeton, 2015
Dropbox shared folder with extra material
Assessment methods and Criteria
Two assignments to be delivered, a written exam and an oral interview