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Empirical Methods in Finance

  • Enseignant(s):   E.Jondeau  
  • Titre en français: Méthodes empiriques en Finance
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre de printemps 2018-2019, 4.0h. de cours + 2.0h. d'exercices (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formations concernées:
    Maîtrise universitaire ès Sciences en finance : Entrepreneuriat financier et science des données

    Maîtrise universitaire ès Sciences en finance, Orientation finance d'entreprise

    Maîtrise universitaire ès Sciences en finance, Orientation gestion des actifs et des risques

 

Objectifs

The aim of the course is to provide a comprehensive knowledge of the econometric tools that are essential to estimate financial models, for asset pricing, asset management, or risk management. We focus on the empirical techniques used most often in the analysis of financial markets and how they are applied to actual market data.

There are two major components in this course. First, theoretical lectures present the main topics and estimation techniques. Second, empirical sessions aim at implementing the techniques on actual data for addressing financial issues. For instance, for the session on volatility modeling, the objective will be to estimate a model with time-varying volatility, allowing allocating a portfolio using a mean-variance criterion.

Contenus

We aim at treating the following topics. However, given the available time, not all topics may be covered:

[1] Characteristics of financial time series

[2] Univariate Time Series Analysis

[3] Multivariate Time Series Analysis

[4] Non-Stationarity and Cointegration

[5] Capital Asset Pricing Model

[6] Multi-factor Models

[7] Efficient markets hypothesis

[8] Modeling volatility: ARCH models

[9] Modeling volatility: GARCH models

[10] Modeling non-normality

[11] Extreme value theory

[12] Modeling correlation

[13] Copula models

Références

Jondeau, E., S.-H. Poon, and M. Rockinger (2006), Financial Modeling Under Non-Normality, Springer Finance. It is an advanced text book for both parts of course.

Campbell, J. Y., A. W. Lo, and A. C. MacKinlay (1997), The Econometrics of Financial Markets, Princeton University Press. Covers topics in the first part of the course.

Pré-requis

Data Science for Finance

Evaluation

1ère tentative

Examen:
Ecrit 3h00 heures
Documentation:
Non autorisée
Calculatrice:
Autorisée avec restrictions
Evaluation:

Homework: Homework will typically consist in the implementation of the techniques studied during the lectures. Occasionally the problem sets will ask to cover assigned readings or methods that were not sufficiently covered in class.

Let HG be the grade for the assignments. It will be the simple average of the grades obtained for the assignments. All the assignments will be taken into account in the average.

Mid-Term: There will be a 2-hour exam in April. The grade that you get for this exam will be called MTG.

Final Examination: The final exam will be a comprehensive 3-hour exam. We call this grade FEG.

The overall grade will be given by the formula

25%*HG + 25%*MTG + 50%*FEG.

Rattrapage

Examen:
Ecrit 3h00 heures
Documentation:
Non autorisée
Calculatrice:
Autorisée avec restrictions
Evaluation:

If you need to retake the exam, the grade will be simply based on the make-up exam, i.e. the mid-term exam and the homework no longer count.



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