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Empirical Methods in Finance

  • Teacher(s):   E.Jondeau  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2019-2020, 4.0h. course + 2.0h exercices (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Finance, Orientation Asset and Risk Management

    Master of Science (MSc) in Finance, Orientation Corporate Finance

    Master of Science (MSc) in Finance : Financial Entrepreneurship and Data Science

[warning] This course syllabus is currently edited by the professor in charge. Please come back in a few days. --- For your information only, here is the old syllabus :

Objectives

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.

Contents

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

References

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.

Pre-requisites

Data Science for Finance

Evaluation


 

First attempt


 
Exam:
Written 3h00 hours
Documentation:
Not allowed
Calculator:
Allowed with 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.


 

Retake


 
Exam:
Written 3h00 hours
Documentation:
Not allowed
Calculator:
Allowed with 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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