Durée
30h Th
Nombre de crédits
Enseignant
Langue(s) de l'unité d'enseignement
Langue anglaise
Organisation et évaluation
Enseignement au premier quadrimestre, examen en janvier
Horaire
Unités d'enseignement prérequises et corequises
Les unités prérequises ou corequises sont présentées au sein de chaque programme
Contenus de l'unité d'enseignement
"Financial Risk Modeling" introduces students to a set of techniques for modeling market risks in financial portfolios. As initially emphasized by Markowitz, the two relevant characteristics of an asset portfolio are its expected return and the dispersion of possible returns around the expected return, i.e. the standard deviation of returns. Presuming risk aversion, rational investors will choose to hold efficient portfolios, i.e. those that maximize the expected return for a given degree of risk or, alternatively, minimize risk for a given level of expected return.
The course is both about risk management and risk measurement and modeling. It not only extends the framework of Markovitz but also presents a few real-life, practical issues in financial risk management. It also demonstrates how stochastic processes can help complement the risk measurement and modeling.
The course focuses on the link between mathematical ingredients and business needs. It underlines the need for advanced mathematical techniques in risk modeling.
Applications are performed in Excel as well as in the R language, to which students are introduced.
Covered topics include:
- Volatility: properties, equally weighted moving average measurement pitfalls, exponentially weighted moving average estimation, volatility clusters, volatility as a GARCH process
- Other measures of risk: Value-at-Risk, Tail Value-at-Risk
- Stochastic modeling: stochastic differential equations, derivative pricing
Please note that the content and the order may vary according to the student group dynamics.
Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement
The following abilities are developed in this course:
(1) Students will strengthen their knowledge and understanding of financial risk management and rely on their knowledge to perform a rigorous analysis of a management situation. They will design optimal and creative solutions (through modeling methods).
(2) They will gain knowledge and understanding of financial engineering and be able to mobilize them in order to implement solutions to concrete management problems or cases.
(3) They will communicate about financial risk management problems in English and develop team work abilities.
(4) They will autonomously acquire knowledge by reading scientific articles; they will present their findings to their peers
Specific skills and competencies are trained during this course.
Students will be able to:
- discuss properties of volatility and Value-at-Risk models
- model Value-at-Risk and volatility using a framework appropriate to the application under consideration
- discuss limitations of models and their contribution to model risk
- understand and explain how stochastic processes contribute to the modeling of typical financial risk management problems
- exemplify this contribution on concrete cases
- describe in business terms the mathematical setups needed for asset pricing, asset value prospective simulation, and VaR estimation of a random financial variable in the future
- perform simulations and valuations associated to stochastic processes by implementing elementary models in a high-level programming language.
Savoirs et compétences prérequis
Students attending this course are expected to have a good background in investment and portfolio management and have a good understanding of asset pricing models. For the second part of the course, students should be familiar with basic probability, statistics, and linear algebra concepts and methods.
Activités d'apprentissage prévues et méthodes d'enseignement
The course is lecture-style with active discussions about practical examples. The course organizes "computer labs" for applying the studied concepts on practical case-studies. Students will work on 2 group projects. They are invited to work in groups of 2 or 3 students. Each group will present an imposed topic to the class, on the one hand, and will implement a mathematical model, on the other hand.
Mode d'enseignement (présentiel ; enseignement à distance)
The course is structured into face-to-face lectures, computer labs and group-meetings.
Lectures recommandées ou obligatoires et notes de cours
The recommended textbook is:
Market Risk Analysis: Practical Financial Econometrics (Vol. II) by Carol Alexander, Wiley, 2008, ISBN-13: 978-0-470-99801-4
Slides and information about the courses can be found on the lol@ platform http://lola.hec.uliege.be
Modalités d'évaluation et critères
The final grade will be determined by :
1. Group presentation of a topic: 35%
2. Group programming project: 15%
3. Individual oral exam: 50%
Stage(s)
none
Remarques organisationnelles
Contacts
Affiliate Prof. Fabien Boniver - email: f.boniver@uliege.be
Please schedule an appointment by email!
Adaptation des engagements pédagogiques suite à la pandémie de COVID-19 pour la session de mai-juin
Méthodes d'apprentissage mises en œuvre : enseignement à distance
Matière de l'évaluation
Méthodes d'évaluation
Contact
Adaptation des engagements pédagogiques suite à la pandémie de COVID-19 pour la session août-sept
Matière de l'évaluation
Méthodes d'évaluation (et plateforme utilisée)
Contact(s)
Notes en ligne
online notes
The core materials for the course consist of the required textbook readings. Lecture notes will be available on the course web page (on lol@). Other items such as problem sets will also be available on the course web page. Some additional readings on materials related to the course over the term may be provided throughout the course via the course web page.