MSc Quantitative Finance

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £22,600
  International, including EU, students (per annum): £36,800

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

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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.

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

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 unit aims 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.

Introduction to Time Series

Univariate Time Series

Multivariate Time Series

GARCH Models

High-Frequency Financial Econometrics

Applications of Volatility Modelling

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 Blackboard. Post-lecture recording is enabled, allowing students to review the lectures after class.

Practical lecture: 2-hour weekly online synchronous zoom 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.

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.

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

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.

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

Examination - 60%

Group Coursework - 40%

Marking and feedback available 15 working days

Core Text

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 Blackboard. You should direct all questions regarding course content to the online forum.