Financial Risk Modeling

Duration

30h Th

Number of credits

Lecturer

Fabien Boniver

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

"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.

Learning outcomes of the learning unit

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.

Prerequisite knowledge and skills

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.

Planned learning activities and teaching methods

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 of delivery (face to face, distance learning, hybrid learning)

  The course is structured into face-to-face lectures, computer labs and group-meetings.

Organisational adjustments related to the current health context

Recommended or required readings

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

 

Assessment methods and criteria

Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.

The final grade will be determined by :
1. Group presentation of a topic: 35%
2. Group programming project: 15%
3. Individual oral exam: 50%

Work placement(s)

none

Organizational remarks

Contacts

Affiliate Prof. Fabien Boniver - email: f.boniver@uliege.be
Please schedule an appointment by email!  

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