Financial Risk Modeling

Duration

30h Th

Number of credits

Lecturer

Julien Hambuckers

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

The course "Financial Risk Modeling" aims to give an overview of modelling concepts related to market, credit and operational risks, in the context of the banking industry. Indeed, in today's highly regulated environment, banks have to comply with stringent risk calculations to determine their regulatory capital. In addition, from a risk management perspectives, banks heavily rely on risks measures to decide upon strategical asset allocation. Therefore, there is a strong need for future professionals in the field of finance to be able to contribute to this process, or at least understand the general machinery.

The course is based on the book "Quantitative Risk Management" (McNeill, Frey an Embrechts, Princenton University Press).

The first part of the course focuses mostly on market risk measurement. Several methods (historical simulation, Monte Carlo methods, GARCH) to compute Value-at-Risk and Expected Shortfall of assets, as well as to backtest these models, will be presented and discussed. Then, the notion of copula will be introduced, to allow discussing portfolio's issues. Exercise sessions will allow the students to put in application these concepts. 

A second part of the course discusses notions of credit risks more into detail, in particular probability of default, loss given default and exposure at default. Structural modeling techniques such as Merton's model are introduced. The problem of including notions related to climate risks and their current scope in the banking regulation, will be introduced through selective readings or interventions from financial industry professionals.

If time permits, elements of operational risk modelling and insurance analytics techniques (e.g. extreme value theory) will be covered.

The exercise session are given with the software R, for which the students are expected to have a first knowledge, although some introductory sessions will be also provided (datacamps and an introductory exercise book are provided to those needing to brush up their knowledge of the software). 

The exact evaluation modalities are presented at the beginning of the semester. The final evaluation is typically a written exam in January, mixing up theory and exercises, and covering all notions seen in classe.

Learning outcomes of the learning unit

The following competences 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 risk management situation. 

(2) They will gain knowledge and understanding of financial engineeringmethods and be able to mobilize them in order to implement solutions to concrete risk modelling problems or cases.

(3) They will communicate efficiently about financial risks in English.

(4) They will autonomously acquire knowledge by reading high scientific-level textbooks.

(5) They will autonomously acquire a working knowledge of the statistical software R to solve financial engineering problems.


Specific skills and competencies are trained during this course.

Students will be able to:

- discuss properties of value-at-risk, expected shortfall, PD, LGD and EAD models.

- model the above mentioned risk indicators using a framework appropriate to the application under consideration.

- discuss limitations of these models, and use adequate backtesting methods to assess those models.

- describe in mathematical terms the risk indicators used, and translate them in business or regulatory notions.

- perform simulations associatd to stochastic processes by implementing these models in the R programming language.

Prerequisite knowledge and skills

Students attending this course are expected to have a good background in statistics, probability, programming, introduction to asset pricing models and financial markets.

Planned learning activities and teaching methods

The course is lecture-style with active discussions about practical examples. Computer labs are also organized to allow students to apply the studied concepts on practical case.
 

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

Face-to-face course


Further information:

  The course is structured into face-to-face lectures (in real-time, no recodring) and computer labs.

Course materials and recommended or required readings

Platform(s) used for course materials:
- LOL@


Further information:

The recommended textbook is:

Quantitative Risk Management: Concepts, Techniques and Tools - Revised Edition Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts Series: Princeton Series in Finance


 

Exam(s) in session

January exam session

- In-person

written exam ( open-ended questions )

August-September exam session

- In-person

written exam ( open-ended questions )


Further information:

The final grade will be determined by a final written exam in January, mixing up practical exercises and theoretical questions. Depending on the number of students, it may be complemented by a group assignment based on the StockTrak trading platform. 

Exact evaluation modalities will be presented at the beginning of the course.

Depending on the number of students, the second session exam is either an oral exam with preparation time, or a written exam similar to the first session exam. If an assignment takes place during the semester, the grade is not transferred to the second session.

Work placement(s)

none

Organisational remarks and main changes to the course

Contacts

Professor: Prof. J. Hambuckers 

Teaching assistant: P. Hubner

 

Association of one or more MOOCs

Items online