Time Series Analysis

MSc. Finance: Elective course in Financial Mathematics or Stochastic

MSc. Mathematics/WiMa: Elective course in Financial Mathematics

2+2 SWS, 6 Credits

News:

• Lecture:  Wednesday, 10:00-12:00, He18 - 1.20
• First Lecture: 19/04/2017
• Exercise class: Friday, 10:00-12:00, He18 - 1.20.
• First Exercise class: 28/04/2017

Final Exam:

oral (no prerequisites)

Prerequisites:

Measure theoretic probability theory.

Contents:

This course covers the basic facts of time series analysis. Time series analysis is concerned with the discription of true data through a stochastic model which is usually assumed to be stationary. The contents of the lecture include:

• Examples of Time Series
• Stationarity of time series
• Estimating trend and seasonal components
• Properties of the autocovariance function
• Linear filters and moving average processes of infinite order
• ARMA models
• Linear prediction
• Estimation of the mean and the autocovariance
• Estimation for causal autoregressive processes
• A short introduction to spectral theory

•           Wold decomposition (if time permits)

 Literature:

A list of reference books would cover the following works:

• P.J. Brockwell and R.A. Davis: Time Series: Theory and Methods, 2nd edition, Springer, 1991.
• P.J. Brockwell and R.A. Davis: Introduction to Time Series and Forecasting, 2nd edition, Springer, 2002.
•   J.-P. Kreiß, G. Neuhaus: Einführung in die Zeitreihenanalyse: Springer, 2006.
• W.A. Fuller: Introduction to Statistical Time Series. 2nd edition, Springer, 1996.

Exercise sheets:

Moodle

Moodle