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| FINA0060-1 | Empirical Methods in Financial Markets
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| Duration : | 30h Th |
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| Number of credits : |
| Master in Economical Sciences, in-depth approach, 1st year | | Second semester | | 5 |
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| Master in Economical Sciences, didactic approach, 1st year | | Second semester | | 5 |
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| Master in Business Engineering, didactic approach, 1st year | | Second semester | | 5 |
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| Master in Economical Sciences, Professional Focus in Economic Policies and Analysis, 1st year | | Second semester | | 5 |
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| Master en sciences économiques,orientation générale, à finalité spécialisée en economics and finance, 1st year | | Second semester | | 5 |
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| Master degree in Business Engineering, professional Focus in Financial Engineering, 1st year | | Second semester | | 5 |
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| Master en sciences économiques, orientation générale, à finalité spécialisée en economic analysis and public governance, 1st year | | Second semester | | 5 |
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| Master in Management Engineering, professional Focus, 1st year | | Second semester | | 5 |
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| Master en ingénieur de gestion, à finalité spécialisée en intrapreneuriat, 1st year | | Second semester | | 5 |
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| Master en ingénieur de gestion, à finalité spécialisée en Modélisation et technologie, 1st year | | Second semester | | 5 |
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| Master in Business Engineering, professional Focus in Supply Chain Management, 1st year | | Second semester | | 5 |
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| Master en ingénieur de gestion, à finalité spécialisée en performance management systems, 1st year | | Second semester | | 5 |
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| Lecturer : | Cédric Heuchenne |
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| Coordinator : | Cédric Heuchenne |
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Language(s) of instruction :
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| English language |
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Course contents :
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| 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
- Causality
- Autoregressive moving average models (ARMA models)
- Generalized autoregressive conditional heteroscedasticity models (GARCH models)
- Kalman filter
- ... |
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Learning outcomes of the course :
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| 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 |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| 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). |
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Mode of delivery (face-to-face ; distance-learning) :
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| Used methodology
A3. Analysis of a practical problem by each student (partially followed up by the teacher).
A4. Critical synthesis of the research, readings and/or practical applications achieved by the student. In principle, each student presents his readings and obtained results at the end of the semester. During his talk, he is invited to
1) clearly present a 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, he 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 three weeks, the teachers present the different problems of interest with the necessary corresponding theoretical basic knowledge. Then, students choose two problems and begin a personal bibliographic research. During the next four weeks, the students and the teacher meet to evaluate the progress of the work and define the remaining steps to achieve. Finally, the students write a report and prepare an oral presentation of their problem for the evaluations period that follows the course.
Decomposition of the student workload
A1 Ex-cathedra lectures 12h
A3 Personal analysis of the problem 70h
A3 Meetings with the teacher 8h
A4 Report 25h
A4 Presentation 5h |
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Recommended or required readings :
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| Introduction syllabus
Advised readings:
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) |
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Assessment methods and criteria :
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| Evaluation tools, evaluation criterions and weighting
E4. Final report (40% of the final note)
The evaluation is based on the clarity, the ability to synthesize and the critical analysis of the students.
E4. Oral presentation (40% of the final note)
1. Quality of the presentation: scientific methodology (10%), slides content (10%) and quality of the explanations (10%).
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 (20%)
Evaluations agenda
The final report has to be sent to the teacher before the evaluations period that follows the course. The oral presentation is held during this evaluations period. |
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Contacts :
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| Cédric HEUCHENNE, HEC-ULg Management School of the University of Liège, B31, local 2.53, email: C.Heuchenne@ulg.ac.be |
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