At the end of this learning unit, the student is able to :
The aim of this course is to give a good comprehension of the theory and application of stochastic time series modelling, with a view towards prediction (forecasting).
2. Linear processes - simple parametric models (ARMA)
3. Estimation and prediction of ARMA models
4. Box-Jenkins analysis - (S)ARIMA models
5. Non-linear processes - heteroscedastic (G)ARCH models - applications to modelling financial data
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Basic models of linear time series will be treated in the first part. The data analysis, i.e. estimation of the model parameters for forecasting, will be based predominantly on Box-Jenkins methods. In the second part of the course some elements of modelling financial data with the more recently developed ARCH and GARCH models will be given and included into the practical part of the course (done with the S-Plus software).
Due to the COVID-19 crisis, the information in this section is particularly likely to change.The examination will be oral. An applied data analysis project has to be prepared on the computer.
Brockwell, P and R. Davis (1991), Time Series, Theory and Methods. Springer, New York
Gourieroux, Ch. (1992), Modèles ARCH et applications financières. Economica, Paris
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