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
This course is an statistical (data) modelling course, aiming at providing students with a fundamental knowledge of regression and statistical modelling techniques with applications in the fields of Finance and Economics, as well as being able to solve these problems with the help of the software R. It ambitions to give students a firm basis in data analysis and statistics for their master thesis. It is also of interest for students targeting quantitative jobs (quant, risk modelling/validation positions, data scientists), aiming at a PhD in economics/finance or wanting to prepare the FRM certification.
The course will mix statistics, financial econometrics and financial engineering classes. Starting from practical data modelling problems, I will explain the kind of statistical models that can be used to solve the problem, the underlying statistical theory and how to implement a solution with R. Then, during practical sessions, the students are expected to work by themselves on related exercises, with the aim to eventually conduct realistic data analyses. During these practical session, instructors (a teaching/student assistant and me) will be available for questions.
Regarding the statistics/financial econometrics part, I will cover topics related to classical and advanced statistics/regression techniques (linear regression, Generalized Linear Model) and machine learning methods (LASSO, random forest). I will also discuss notions related to empirical study in social sciences, and detail how to conduct prediction/classification tasks. Related problems such as inference and model selection will also be covered.
Regarding applications, topics related to predictive regression analysis (for linear and nonlinear models), classification problems and optiimal portfolio allocation will be considered. This list is open to suggestions and will evolve according to students' proficiency and interests.
The final grade ronsists in a data analysis project per group of two students, either choosen by the students (after acceptance by me) and motivated by an existing research question in the financial or economic literature, or imposed by the lecturer. In the former, examples include the replication of an existing paper or the first step of a master thesis. In the latter case, examples of previous projects were related to portfolio optimization with machine learning methods, but it might evolve depending on the interests of the students. The students will have to hand out a term paper detailing the data analysis, and defend it in front of the class during a seminar session (this will take place either during the last class, or the week after). There is no written exam in January. This grade might be complemented by one programming test on R over the course of the lecture, or an intermediate presentation related to the group work (details will be announced at the beginning of the class).
The second session will be an oral exam combining an imposed data analysis to conduct with R and theory questions. The theory questions will extensively cover the theoretical content of the course.
The course is taught in English.
Learning outcomes of the learning unit
At the end of this course, the students are expected to:
- have a theoretical understanding of advanced regression and time series models, and to be able to explain the theoretical foundations of these models,
- understand the notions of model selection and high dimensionality problems, and to be able to explain these concepts,
- identify the various situations and types of data for which these models are useful (binary or categorical outcomes, daily time series, ratio, etc.), and be able to explain why they chose a particular model to analyse these data.
- be able to write scripts in R, using existing routines or implementing their own solution, to estimate these models and conduct a data modelling exercise,
- be able to read and understand scientific methodological articles, to implement the proposed methodology.
In addition, the students will develop the following competences:
- use of a foreign language (English, with emphasis on a scientific vocabulary),
- writing of a term paper,
- ability to present oraly scientific concepts,
- ability to work autonomously.
Prerequisite knowledge and skills
- Advanced knowledge and interest for statistics and programming. In particular, a good understanding of bachelor courses like "Probabilite et Inference statistiques", "Models and Methods in Applied Statistics" or "Econometrics" is a prerequisite for the statistical side.
- Courses or self-teaching of the fundamentals of programs like R, VBA or Python is a good basis. Learning material for a crash-course in R will be provided, relying on the learning platform Datacamp (https://www.datacamp.com/)
- Courses like Advanced Econometrics, Empirical Methods in Financial Markets, Seminar in Applied Econometrics, Financial Risk Modelling are good pre-requisites or complements.
Planned learning activities and teaching methods
Ex-cathedra lectures (theory), exercises, oral presentation during seminars, self reading of scientific articles.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face.
Course materials and recommended or required readings
The main references will be:
The Elements of. Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. February 2009. Trevor Hastie · Robert Tibshirani.
An Introduction to Statistical Learning: with Applications in R, by G. James et al. (2013).
Regression: Models, Methods and Applications. Berlin: Springer-Verlag. by Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan; Marx, Brian (2013).
They will be complemented by slides.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions ) AND oral exam
Written work / report
Out-of-session test(s)
Additional information:
- Written report presenting a data analysis related to the methods seen in class.
- Oral presentation (15 min + questions) during a seminar.
- To be confirmed : programming exercises or intermediate presentation of a research question
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Prof. Julien Hambuckers
email: jhambuckers[at]uliege.be
Teaching assistant: P. Hübner (phubner[at]uliege.be) and P-F. Weyders (pierre-francois.weyders[at]uliege.be)