2023-2024 / FINA0064-1

Financial Risk Modeling


30h Th

Number of credits

 Master in economics : general (120 ECTS)5 crédits 
 Master in business engineering (120 ECTS)5 crédits 
 Master in business engineering (120 ECTS) (Digital Business)5 crédits 
 Master in mathematics (120 ECTS)5 crédits 
 Master in mathematics (60 ECTS)5 crédits 


Julien Hambuckers

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January


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 (considerations related to the insurance industry will also be discussed). 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 professional 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 will be focused 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 session will allow the students to put in application these concepts. 

A second part of the course will discuss 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 will be presented. Several practical sessions will give the students the opportunity to implement these models.

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

A third part of the course will discuss the problem of climate and liquidity risks, their modelling and their current scope in banking regulation, via presentations made by profesionnal risk managers in the banking, risk and insurance industry.

The exercise session will be 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 will be also provided to those needing to brush up their knowledge of the software). 

The final evaluation is a zritten exam, mixnig up theory and exercises, and covering all notions seen in classe. The student will have to conduct a risk modelling and regulatory capital calculation exercise. General theoretical questions will be also asked at that occasion. The exact evaluation modalities will be presented at the beginning of the semester.

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 risk management situation. 

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

(3) They will communicate about financial risk  in English.

(4) They will autonomously acquire knowledge by reading scientific articles.

(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 applying the studied concepts on practical case.

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

Face-to-face course

Additional information:

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

Recommended or required readings

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 ) AND oral exam

Written work / report

Additional information:

The final grade will be determined by at least a recap exercise to be done in group (20%), and a final written exam in January, mixing up practical exercises and theoretical questions (80%). Exact evaluation modalities will be presented at the beginning of the course.

The second session is an oral exam with preparation time. This exam counts for 100% of the final grade (no transfer of the grade for the group work from one session to the other).

Work placement(s)


Organisational remarks and main changes to the course


Main lecturer: Prof. J. Hambuckers 

Teaching assistant: P. Hubner


Association of one or more MOOCs

Items online

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.