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Unit information: Advanced Time Series in 2016/17

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Advanced Time Series
Unit code MATHM6003
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 2C (weeks 13 - 18)
Unit director Dr. Cho
Open unit status Not open

MATH33800 Times Series Analysis and MATH20800 Statistics 2.



School/department School of Mathematics
Faculty Faculty of Science


Unit aims

Describe limitations of stationary linear time series models Introduce and describe ARCH and GARCH financial time series model. Introduce and describe locally stationary time series models. General Description of the Unit

This course builds on the Level 6 MATH33800 Time Series Analysis course that described classical stationary linear time series analysis. This course considers the suitability of classical models in a variety of settings. The course then divides naturally into two sections: 1. models which possess time-varying conditional variances (GARCH, ARCH) and 2. locally stationary time series. We introduce ARCH/GARCH models, examine their properties and methods for fitting and model criticism. We then introduce locally stationary models, examine their properties and explore methods for estimating key quantities. Real life data examples will be provided throughout where necessary.

Relation to Other Units

This course builds on MATH33800, Time Series Analysis.

Additional unit information can be found at

Intended learning outcomes

Learning Objectives

At the end of the unit students should be able to:

  • Model and fit simple ARCH/GARCH models
  • Model and estimate key parameters for locally stationary processes
  • Describe the key details relating to ARCH/GARCH models.
  • Describe the key details relating to locally stationary models.

Transferable Skills

The students will gain experience of modelling and fitting advanced time series models to data. These skills are highly valued in a number of areas but especially financial data modelling.

Teaching details

Lectures (with encouraged audience participation) plus problem and solution sheets. Some of the questions on the problem sheets will be to do with practical data analysis.

Assessment Details

100% Examination.

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

Reading and References

Reading and references are available at