Duration
30h Th
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
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. The problem of including notions related to climate and liquidity risks in credit ratings, as well as the modelling of those specific risks and their current scope in the banking regulation, will be introduced through presentations made by profesionnal risk managers in the banking, risk and insurance industry.
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.
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 exact evaluation modalities will be presented at the beginning of the semester. Depending on the number of participants, the final evaluation will be either a written exam in January, mixing up theory and exercises, and covering all notions seen in classe, or an assignment with oral presentation, covering the same topics, to be handed out at the end of the semester. In both cases, the student will have to conduct a risk modelling and regulatory capital calculation exercise. General theoretical questions will be also asked at that occasion.
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.
Course materials and 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 ) AND oral exam
August-September exam session
- In-person
written exam ( open-ended questions ) AND oral exam
Written work / report
Further information:
Depending on the number of participants, the final grade will be determined by
- either a final written exam in January, mixing up practical exercises and theoretical questions,
- or a group assignment to be presented at the end of the semester, over the same content.
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)
none
Organisational remarks and main changes to the course
Contacts
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.