Applied Time Series
Enseignant responsable :
Volume horaire : 24Description du contenu de l'enseignement :
The objective of the course is to study the theory, modeling, programming, and interpretation of the major time series models. Some applications to finance will be undertaken using Python. At the end of this class, students should be able to :
- Develop knowledge of basic univariate time series models appropriate for economic and financial data.
- Learn how to specify and estimate a time series model on these data.
- Be able to use such models for forecasting and to evaluate their performance.
- Familiarize with common volatility modelling approaches.
Course outline:
1/ Time series building blocks
- Stationarity
- Autocorrelation and white noise
- Testing autocorrelation
- Non-stationarity
- Python exercices
2/ ARMA Framework
- Moving average process
- Auto regressive process
- ARMA models and the Box-Jenkins method
- Maximum-Likelihood estimation
- Simulation and model selection with Python
3/ Specific topics and applications
- Unit-roots
- Trends
- Seasonnality
- Python application to the Earnings-Per-Share
4/ Volatility models
- GARCH
- Value-at-Risk
- Expected Shortfall
- Yahoo-Finance API and ARCH/GARCH modelisation with Python
5/ Principal Component Analysis
- Normed vs. Non-normed PCA
- Contribution and quality of representation of observations and variables on principal components
- Computation of the Absorption Ratio with Python
Pré-requis obligatoires :
Students must be enrolled in course Python Programming and must have past Introduction to Financial Econometrics.
Coefficient : 1.5Compétence à acquérir :
Master the econometrics (dynamic) tools used in empirical finance
Mode de contrôle des connaissances :
Assignment (30%) + Final Exam (70%)
Bibliographie, lectures recommandées
Brooks C (2008), Introductory econometrics for Finance, Cambridge Univ Pr. Brockwell, P.J. and Davis, R.A. (2002), Introduction to time series and forecasting, Springer Verlag. Campbell J., Lo A., McKinley, A. (1997), The Econometrics of Financial Markets. NJ: Princeton University Press. Francq C, Zakoïan J.M. (2010), Garch models: Structure, statistical inference and financial applications, Wiley. Hamilton J. D. (1994), Time Series Analysis, Princeton University Press.