Financial data modelling

Duration

30h Th

Number of credits

Lecturer

Julien Hambuckers

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January

Schedule

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 MATLAB, and showing how this software can be used to tackle financial engineering and financial economic modelling problems. It ambitions to give students a firm basis in programming and statistics to conduct data analysis for their master thesis. It is also of interest for students interested in quantitative jobs (quant, risk modelling/validation positions, data scientists) or aiming at a PhD in economics/finance. 
The course will mix programming, statistics, financial econometrics and financial engineering classes. I will introduce the various statistical concepts starting from data examples and show how to implement a solution with MATLAB. Then the students are expected to work by themselves on related exercises, with the aim to perform some data analysis at some points.  The instructors (a teaching/student assistant and me) will be available for questions.
Regarding MATLAB, the topics covered will be some fundamentals in algorithmic (variable definition, loop,...), simulation techniques, optimization techniques, function definition, some statistical and econometric packages depending on the needs of the students for their master thesis (e.g. GLM and GARCH).
Regarding the statistics/financial econometrics part, I might cover topics related to classical and advanced regression techniques (linear regression, nonparametric regression, Generalized Linear Model - logistic, model selection via LASSO, extreme value theory,  a.o.) and time series econometrics (GARCH models). Regarding applications, topics like risk measures computation (VaR and Expected Shortfall), volatility forecasts, predictive regression analysis (lfor linear and nonlinear models) will be considered. This list is open to suggestions and will evolve according to students' proficiency and interests. 
The final exam consists in conducting a statistical analysis of some data choosen by the student (after acceptance by me), motivated by an existing research question in the financial or economic literature. It can be a replication of an existing paper or the first step of a master thesis. The student will have to hand out a term paper detailing its 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 is complemented by programming tests over the course of the lecture.
The second session will be an oral exam based on an imposed data analysis to perform or on the methodological content of a research article (to be determined).
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 their reasoning.
- be able to write scripts in MATLAB by themselves, 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 programming basis.
- Courses like Advanced Econometrics, Empirical Methods in Financial Markets, Seminar in Applied Econometrics, Financial Risk Modelling are good pre-requisite 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.

Organisational adjustments related to the current health context

Recommended or required readings

tba

Assessment methods and criteria

Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.

- Written report presenting a data analysis related to the methods seen in class.
- Oral presentation (15 min + questions) during a seminar.
- Graded programming exercises (on the spot)

Work placement(s)

Organizational remarks

Contacts

Julien Hambuckers, PhD | Assistant Professor of Finance
email: jhambuckers[at]uliege.be