2023-2024 / FINA0063-1

Advanced Statistical Methods in Finance


30h Th

Number of credits

 Master of Science (MSc) in Data Science5 crédits 
 Master of Science (MSc) in Data Science and Engineering5 crédits 
 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 


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

This course is an advanced statistical (data) modelling course, aiming at providing students with a fundamental knowledge of regression and statistical modelling techniques in the fields of Finance, Economics and Management, 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 what kind of statistical model can be used to solve the problem, what is 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 like 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 exam consists 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 latter case, the project will be related to portfolio optimization with machine learning methods. Other examples include the replication of an existing paper or the first step of a master thesis. 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 based on an imposed data analysis to conduct with R, and 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)


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


Prof. Julien Hambuckers
email: jhambuckers[at]uliege.be

Association of one or more MOOCs