Unit information: Financial Time Series in 2019/20

Unit name	Financial Time Series
Unit code	MATHM0025
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 2D (weeks 19 - 24)
Unit director	Dr. Cho
Open unit status	Not open
Pre-requisites	MATH20800 Statistics 2 and MATH33800 Time Series Analysis
Co-requisites	None
School/department	School of Mathematics
Faculty	Faculty of Science

Description including Unit Aims

Unit Aims

This course aims to provide both rigorous theoretical justifications of GARCH models and error correction models, and also systematic data analysis tools from data visualisation to model evaluation, when GARCH effects or co-integration phenomenon is presented.

Unit Description

This course describes classical stationary linear time series analysis, moves onto non-linear and non-stationary time series with an emphasis in modelling financial time series, and is concluded with a brief introduction on statistical methods used in high-frequency trading.

This course aims to cover

• Theoretical and practical aspects of GARCH financial time series models and variants thereof,
• Theoretical and practical aspects of vector autoregressive co-integration in economic and financial time series models,
• A brief introduction on statistical methods in high-frequency financial time series

Relation to Other Units

This course builds on MATH33800 Time Series Analysis.

Intended Learning Outcomes

Learning Objectives

At the end of the unit the student should be able to

• Understand the theoretical conditions under which the GARCH models have strictly and weakly stationary solutions.
• Understand the sandwich estimator used in the GARCH model estimation and a deeper understanding of the robustness in general statistical procedures.
• Examine if a certain dataset should be modelled as GARCH models, estimate and select GARCH models to fit the data, and evaluate the fitted models through the residuals.
• Examine if a multivariate time series data are co-integrated and model the co-integration.
• Know of basic tools to handle high-frequency financial time series.

Transferable Skills

The ability to know when different time series models work and fit suitable models are highly valued in many areas, especially in finance.

Teaching Information

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

Assessment Information

100% Examination.

1.5 hours

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

Recommended

• Torben G. Andersen, Handbook of Financial Time Series, Springer, 2009
• Peter J. Brockwell and Richard A. Davis, Time Series: Theory and Methods. Springer, 2009
• T.L. Lai and Haipeng Xing, Statistical Models and Methods for Financial Markets, Springer, 2008