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Establishment
Language of instruction
English
Teaching content
FINANCE
This course occurs in the following program(s)
MSc in Finance
- Crédits ECTS: 2.00
Training officer(s)
F.CASALIN
Stakeholder(s)
F.CASALIN
Présentation
Prerequisite
Students are expected to have a general understanding of the following concepts: random variables; basic probability distributions such as Gaussian, Student t, Chi-square and Fisher distributions; statistics such as expectation/mean, variance/standard deviation, correlation; hypothesis testing, p-values (i.e. critical thresholds for tests) and confidence intervals.
Goal
Breakdown complex organizational problems using the appropriate methodology
Construct expert knowledge from cutting-edge information
Demonstrate an expertise on key concepts, techniques and trends in their professional fieldBe a reference point for expertise-related questions and ambiguities
Construct expert knowledge from cutting-edge information
Demonstrate an expertise on key concepts, techniques and trends in their professional fieldBe a reference point for expertise-related questions and ambiguities
Presentation
Based on real datasets and practical applications (on Excel and Eviews), the lectures will cover the following topics:
- Correlation tests to check for linearity between variables;
- Simple linear regression;
- Multiple linear regression;
- Graphical investigation of breaks in time series and usage of dummy variables;
- Validation of regressions (i.e. fulfillment of key OLS assumptions, residuals’ autocorrelation and heteroskedasticity)
- Use and limitations of linear regressions
- Correlation tests to check for linearity between variables;
- Simple linear regression;
- Multiple linear regression;
- Graphical investigation of breaks in time series and usage of dummy variables;
- Validation of regressions (i.e. fulfillment of key OLS assumptions, residuals’ autocorrelation and heteroskedasticity)
- Use and limitations of linear regressions
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours interactif | 16,00 | In class applications with Eviews and Excel | |
| Autoformation | |||
| E-Learning | 8,00 | Through the use of Excel and Eviews | |
| Travail personnel | |||
| Group Project | 16,00 | ||
| Charge de travail personnel indicative | 10,00 | ||
| Overall student workload | 50,00 | ||
Evaluation
1) Financial econometrics project (each group of students works on a selected dataset).
2) A final exam composed of problems, course questions (students can bring a two-sided A4 cheat sheet only devoted to valuation formulas), and in class applications using Eviews to analyse a given dataset.
2) A final exam composed of problems, course questions (students can bring a two-sided A4 cheat sheet only devoted to valuation formulas), and in class applications using Eviews to analyse a given dataset.
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Examen (final) | |||
| Examen écrit | 2,00 | 1 | 50,00 |
| Autres | |||
| Projet Collectif | 8,00 | 1 | 50,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
R.A. DeFusco, D.W. McLeavey, J.E. Pinto, M.J.P. Anson, D.E. Runkle, 2015, Quantitative Investment Analysis, 3rd Edition, Wiley -