MSc Quantitative Finance

Year of entry: 2023

Course unit details:
Time Series Econometrics

Course unit fact file
Unit code BMAN71122
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Alliance Manchester Business School
Available as a free choice unit? No


Time series data is heavily exploited in empirical and quantitative finance as historical information contained in past data can be useful in predicting future behaviour of financial markets. This leads to the development of time series econometrics, a subject dedicated to modelling, analysing and forecasting time series data. In modern financial markets, time series methods play a central role in technical analysis of asset pricing, risk management and portfolio management.

This course begins with an overview of some stylized facts of financial time series data, followed by a rigorous and comprehensive treatment on the theory of time series. The course continues with a series of lectures covering classical univariate and multivariate time series models such as ARIMA, VAR and GARCH, and extending to advanced topics such as high-frequency financial econometrics and regime-switching models. Each lecture is accompanied with a MATLAB session to demonstrate real data application of the covered models.


BMAN71122 Programme Req: BMAN71122 is only available as a core unit to students on MSc Finance and MSc Quantitative Finance, and as an elective to students on MSc Accounting & Finance


The aims of this course are to introduce students to important econometric techniques that are used in time series analysis and to facilitate awareness in students of how these techniques can be used and applied in empirical finance.

Learning outcomes

On completion of this unit successful students will have achieved the following learning outcomes:

  • A detailed knowledge and understanding of advanced techniques and skills in time series Econometrics
  • A systematic knowledge and understanding of issues at the forefront of research a practice in finance
  • A knowledge and understanding of basic research skills and empirical methods in finance
  • The working knowledge of MATLAB programming and implementation of time series methods

Assessment methods

Method Weight
Other 20%
Written assignment (inc essay) 80%

Examination - 80%

Group Coursework - 20%

Feedback methods

Informal advice and discussion during a lecture, seminar, workshop or lab.

Online exercises and quizzes delivered through the Blackboard course space.

Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum.

Written and/or verbal comments on assessed or non-assessed coursework.

Generic feedback posted on Blackboard regarding overall examination performance.

Recommended reading

Peter J. Brockwell & Richard A. Davis (2016), Introduction to Time Series and Forecasting, 3rd edition, Springer

Taylor, S. J. (2009) Asset Price Dynamics, Volatility, and Prediction. Princeton University Press. Princeton.

These texts cover the majority of the material delivered in this course unit. It is strongly advised that you purchase these texts. Older versions of the texts also cover the most of the material.

In addition to these recommended texts you should undertake supplementary reading of appropriate econometric texts where necessary to support your learning. In particular, you may find the following texts useful:

Ruey S. Tsay (2010) 3rd ed., Analysis of Financial Time Series, Wiley.

Mikosch, T., Kreiß, J. P., Davis, R. A., and Andersen, T. G. (2009) Handbook of financial time series. Berlin: Springer.

Brockwell, Peter J. & Davis, Richard A. (1991) Time series: theory and methods.

2nd ed. New York, Springer.

Study hours

Scheduled activity hours
Assessment written exam 3
Lectures 30
Independent study hours
Independent study 117

Teaching staff

Staff member Role
Aleksey Kolokolov Unit coordinator
Sungjun Cho Unit coordinator
Yifan Li Unit coordinator

Additional notes

Informal Contact Methods

Office Hours

Online Learning Activities (Blogs, discussions, self-assessment questions)

Drop in Surgeries (extra help sessions for students on material they may be struggling with)

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