Financial Risk

Teacher(s)

Nguyen Anh; Thewissen James;

Language

English

Prerequisites

1. Fundamental mathematical and statistical concepts (such as those covered in Mathématiques avancées et fondements d'économétrie [ LECGE1337 ])
2. Advanced Finance [LLSMS2100A or LLSMS2100B]
3. Corporate Finance [ LLSMS2010 ]

Main themes

This course is an introductory course to banking and financial markets. The financial market part introduces the main concepts of the current financial system, its main instruments and the risk associated with them. The banking part covers banks architecture, balance sheets and bank’s valuation. Students will have the opportunity to apply the theoretical concepts in empirical assignments.

Learning outcomes

At the end of this learning unit, the student is able to :
1	Having regard to the LO of the programme, this activity contributes to the development and acquisition of the following LO KNOWLEDGE AND REASONING 2.2 Master highly specific knowledge in one or two areas of management: advanced and current research-based knowledge and methods. A SCIENTIFIC AND SYSTEMATIC APPROACH 3.3 Consider problems using a systemic and holistic approach: recognize the different aspects of the situation and their interactions in a dynamic process. WORK EFFECTIVELY IN AN INTERNATIONAL AND MULTICULTURAL ENVIRONMENT 5.2 Understand the international socio-economic dimensions of an organization and identify the associated strategic issues and operational decisions. TEAMWORK AND LEADERSHIP 6.1 Join in and collaborate with team members. Be open and take into consideration the different points of view and ways of thinking, manage differences and conflicts constructively, accept diversity.

Content

The goal of this course is to initiate Masters students to a better understanding of Financial Risks in corporations. Instead of providing an exhaustive overview of the field, students will form groups and tackled a specific topic in financial risks. Each group will tackle this topic from a different perspective in the form of a Mini Thesis of 5,000 words maximum. The topic wil be decided at the beginning of the year in function of the current interests of the press, academia and regulators. In addition to covering a specific topic from different persectives, this course discusses and illustrates different empirical approaches to tackle the research questions of the MiniThesis in R and prepares the student for the actual Master Thesis. In particular, using papers on this specific topic, the course will highlight the following empirical topics: endogeneity, difference in difference estimators and event studies.

Teaching methods

Financial Risk is a course in the International Finance track. It is organized in a way for students to question critically the impact of financial risks on corporations' value or managers' behavior and test these questions empirically. After taking this course, students should have a good overview of standard empirical approaches to research questions and a grasp of the corresponding underlying theory in international finance. They should also have a clear understanding of what the state of the art is in the topics we cover and where the contributions stand. Material covered in other international financial management courses, e.g. LLSMS2029 International Financial Management has direct relevance to this course. In particular, topics relating to credit risk, interest rate risk and currency risk provide a useful foundation for understanding the topics at a more advanced level. The students will also learn to communicate research ideas and issues verbally in a clear and logical manner through in-class presentation. Knowledge of the program R or Python is assumed and help/support will be provided throughout the semester.

Evaluation methods

• Students are expected to write a MiniThesis of 5,000 words maximum for this course. The short length of this thesis does not reduce the amount of work. On the contrary, the short length aims at learning how to increase the impact of the writing and communication. The students will for groups of maximum 5 people. Depending on the number of people attending the course, this limitation might change. The MiniThesis will be conducted in a time frame of six weeks and include sections regarding the 1) Motivation/introduction, 2) contribution, 3) Data & method, 4) Result, 5) robustness tests and 6) Conclusion/Discussion. The students will have to organize themselves to collect their own data on a specific topic and conduct the analyses in R. The students will present their research proposal in the third week and defend their findings in the final week of the course with a podcast. Each group will be evaluated by their peers on a list of pre-defined criteria. 
• The final grade is distributed as follows: 
  • 15% – 5-min presentation the review of the literature (peer-reviewed but adjustable according to the instructor's discretion).
  • 30% – 5 min presentation on your methodolgy and findings in the form of a podcast. Originality is key! (peer-reviewed but adjustable according to the instructor's discretion)
  • 35% Final report: A 5,000-word paper that summarizes your research question (peer-reviewed but adjustable according to the instructor's discretion).
  • 20% Active presence & participation in class (peer-reviewed but adjustable according to the instructor's discretion). To avoid free-riders in the group, an intra-group evaluation will be performed. Bad scores will downgrade your own grade. If you do not fully peer-review all the groups, your grade will be reduced by 40% for that part.
  • To avoid any abuse or manipulation, the final grade remains at the instructors' discretion, which is final.
  • Active participation in class is mandatory. Pay attention to the fact that if you are absent from all the classes, your final grade is capped to 12/20 ! 

Online resources

You need to refer to the slides on Moodle, your prior courses and your own originality.
Students must acknowledge the use of artificial intelligence (AI) in the text for the parts where they have used it. They must clearly cite the paragraphs/parts where AI has been used. A failure to acknowledge the use of AI is considered fraud if detected by the similarity detection tools used by the instructor.
The group members are all jointly responsible for the work that they hand in on Moodle and are informed that this work is part of the formal examination of the course.  

Bibliography

Faculty or entity

> CLSM