MSc Finance / Course details

Year of entry: 2025

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
Available as a free choice unit? No

Overview

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 applications of volatility modelling. Each lecture is accompanied by a MATLAB session to demonstrate real data application of the covered models.

Pre/co-requisites

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

Aims

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

Teaching and learning methods

Theory lecture: 3-hour weekly on-campus lecture. This is the main teaching sessions delivered in class by the course co-ordinators, covering all theoretical key points of the course unit. Relevant teaching material and further readings will be provided on Canvas. Post-lecture recording is enabled, allowing students to review the lectures after class.

Practical lecture: 2-hour weekly online synchronous lecture. The practical lectures aim to discuss and guide students through the weekly practice questions. It also provides direct contact hours with the course co-ordinates for students to receive feedback and evaluate their learning progress. Each practical lecture will be recorded with appropriate captions, allowing students to re-watch the session.  

Computer labs: weekly 1-hour computer labs in small groups, delivered physically in AMBS PC cluster rooms. The lab sessions are designed to teach students how to implement the various theoretical econometric models to real-life data using MATLAB. The labs sessions involve a set of tailored weekly lab exercises, which will be discussed interactively in each session 

Knowledge and understanding

Apply detailed knowledge and understanding of data description, model construction, estimation, and inference for financial time-series data. 
Analyse systematic knowledge and understanding of issues at the forefront of research and practice in financial econometrics.
Apply basic research skills and empirical methods to address research questions in quantitative finance with time series analysis. 

Intellectual skills

Analyse analytical skills to understand, derive, and prove theoretical results for basic time-series models. 

Practical skills

Apply MATLAB programming knowledge to implement advanced time series models, such as ARIMA, VAR, GARCH, and high-frequency risk measures. 
Construct models and forecast real-life financial time series for financial return modelling, volatility forecasting, and risk management.

Transferable skills and personal qualities

Collect and analyse empirical financial data on exchange-traded stocks, including stock returns and spreads, for economic prediction using time-series methods.

Assessment methods

Examination - 60%

Group Coursework - 40%

Feedback methods

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

Online exercises and quizzes delivered through the Canvas 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 Canvas 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.


Linton, O (2024) Time Series for Economics and Finance, Cambridge University Press, Cambridge.
 

These texts cover the majority of the material delivered in this course unit. All books are also available physically or electronically from the library.

 

Supplementary text 
 

In addition to the core 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:


Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Berlin Heidelberg.  
 

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.


All teaching materials, handouts, datasets, etc. will be available from Blackboard and additional announcements and discussion questions will be posted on Canvas. You should direct all questions regarding course content to the online forum.

Study hours

Scheduled activity hours
Assessment written exam 1
Lectures 30
Practical classes & workshops 30
Independent study hours
Independent study 89

Teaching staff

Staff member Role
Aleksey Kolokolov Unit coordinator
Yifan Li Unit coordinator

Additional notes

Informal Contact Methods

Office Hours

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

 

Return to course details