|Holder||Prof. Cédric Heuchenne (HEC Liège – ULiège)|
|Location||HEC Liège – Management School of the University of Liège. Rue Louvrex 14, Liège|
|Field||Information Systems/ Supply Chain Management/Management Quantitative Methods (IS/SCM/MQM)|
The financial world shows a deeper and deeper interest for quantitative forecasting methods. For the broker, having good approximations of future values of his equity portfolio is essential. A financial analyst should always anticipate as well as possible the behaviour of firms in which his clients are likely to invest. In this framework, this course develops different existing methods to treat those problems. Its content heavily depends on students’ interests and their professional expectations. Among others, topics in the sequel can be involved :
– Forecasting of seasonal data
– Risk management
– Autoregressive moving average models (ARMA models)
– Generalized autoregressive conditional heteroscedasticity models (GARCH models)
– Kalman filter
Learning outcomes of the course
P2. Application of basic statistical methods to stochastic processes
C3. Analysis, identification of common denominators in the different methods
C4. Critical analysis of existing methods with respect to practical situations
These learning objectives are part of and precise the following Intended Learning Outcomes of the doctoral training program:
- Understand, master and compare the main theories relating to their area of research
- Make appropriate use of the different theories
- Gain autonomy
- Demonstrate critical thinking
Prerequisites and co-requisites/ Recommended optional programme components
1) Basic course in probability (cumulative distribution function, density, distribution, mean, variance, usual discrete and continuous univariate laws, multivariate normal) and statistical inference (estimation , confidence intervals, hypothesis tests). Equivalent to the content of the course: Probability and statistical inference STAT1208-1.
2) Course of quantitative methods in management: mainly multiple regression, maximum likelihood estimation and principal component analysis. For example, this content is studied in
STAT0800-1 Models and Methods in Applied Statistics, or
MQGE0005 Quantitative Methods in Management (Partim Statistics).
Mode of delivery (face-to-face; distance-learning)
A3. Analysis of a practical problem by each group of students (partially followed up by the teacher). Each problem is divided into a number of subproblems corresponding to the number of students of each group.
A4. Critical synthesis of searches, readings and/or practical applications achieved by each group of students. At the end of the semester, each student presents his own readings (corresponding to his subproblem) and then, each group discusses, compares the different methods and presents possible obtained results.
During his talk, each group is invited to
1) clearly present the problem of interest in its financial context and the existing methods to solve it,
2) discuss those methods and justify the choice of one or several of them in specific cases.
Moreover, each student is expected to attend to presentations of the other students and discuss the way they treat their own problem.
Overview of the course agenda
The course is taught during ten weeks. The first four weeks, the teacher presents the different problems of interest with the necessary corresponding theoretical basic knowledge. Then, students (in groups) choose a problem and begin a personal bibliographic search (in agreement with their group -shared bibliographic search-). During the next four weeks, the students and the teacher meet to assess the progress of the work, share and compare readings and define the remaining steps to achieve. Finally, the students prepare an oral presentation of their problem and write a report for the evaluations period that follows the course.
Decomposition of the student workload
A1 Ex-cathedra lectures 12h
A3 Analysis of the problem 70h
A3 State of the progress, meetings with the teacher 8h
A4 Report 25h
A4 Presentation 5h
Recommended or required readings
Introduction syllabus and slides (useful to understand proposed works)
1. Brockwell, P. J., & Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York : Springer.
2. Franses, P. H. (1998). Time series models for business and economic forecasting. Cambridge University Press.
3. Mills, T. C. (1999). The Econometric Modelling of Financial Time Series (Second ed.). Cambridge University Press.
4. Advised readings (according to each student)
Assessment methods and criteria
Evaluation tools, evaluation criterions and weighting
E4. Final report (30% of the final note, common evaluation)
The evaluation is based on the clarity, the ability to synthesize and the critical analysis of the students.
E4. Oral presentation (50% of the final note, 40% individual, 10% common)
1. Quality of the presentation:
quality of the slides (10%, common),
scientific methodology (15%) and quality of the explanations (15%).
Each student identifies and presents its own work in the bibliographical search.
2. Defence of the work: answers to the questions of the teacher and the other students (10%).
E4. Attending and discussing the works of the other students, quality of asked questions (20%, individual evaluation)
Relative weighting of individual assessment: 60%
The final report has to be sent to the teacher before the evaluations period that follows the course. The oral presentation is usually held during the courses period.
Teaching language: English
Cédric HEUCHENNE, HEC Liège, Management School of the University of Liège, N1, Office 309, email: C.Heuchenne@uliege.be
Academic Year 2022-2023
From September 23 to December 2, 2022
The first session will be on September 23 (08:30 – 11:30)
All other sessions will be coordinated with each PhD Candidate based on their projects. Candidates can register to the course until October 15, 2022. To register, please contact Professor Heuchenne directly: C.Heuchenne@uliege.be