Ce cours apparaît dans les formations suivantes :
• IÉSEG > IESEG Degree - Programme Grande École > Semester 2 > 2,00 ECTS

Pré requis

- Specificity and functioning of the various financial markets
- Basics of pricing securities, especially stocks and bonds
- Intermediate level in econometrics (multiple regression with the method of Ordinary Least Squares; OLS assumptions - homoskedasticity, multicollinearity, autocorrelation of error terms; inference and hypothesis testing)
- Some knowledge of Econometrics software is encouraged (example: EViews, R, Matlab)

Objectifs du cours

- Program specific Objectives: Be competent in the field of finance, rigorous and committed to quality (Master the appropriate techniques and display expertise in the field)
- Understand and interpret the main characteristics of financial time series and data
- Analyze in depth the movements of asset price dynamics, returns and volatility
- Compare and contrast the performance of different econometric techniques to model financial data
- Learn practical tools and econometric software to apply theory into practice
- Designing risk assessment frameworks to apply for potential managerial solutions

Contenu du cours

- Session 1: Introductory Background and Fundamentals Concepts
- Session 2: Market Efficiency
- Session 3: ARMA Models
- Session 4: Divident-Price Cointegration and Empirical Tests
- Session 5: Volatility Models
- Session 6: Econometric Tools for Risk management and Measurement

Modalités d'enseignement

Organisation du cours

Méthodes pédagogiques

• Interactive class
• Project work
• Research

Évaluation

Short written exam + final project

Bibliographie

• Brooks, C. (2014). Introductory Econometrics for Finance, 3rd Edition, Cambridge University Press. -

• Wooldridge, J. (2012). Introductory Econometrics : A Modern Approach, 5th Edition, South-Western. -

• Greene, W.H. (2011). Econometric Analysis : International Edition, Pearson Education. -

• Cryer, J.D., and Kung-Sik, C. (2008). Time Series Analysis With Applications in R, 2nd Edition, Springer Texts in Statistics, Springer. -