Time Series Econometrics

BMAN71122 Time Series Econometrics 15 Semester 2 Postgraduate Taught Level 7 Yifan Li Unit coordinator Alliance Manchester Business School 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 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. 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.  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. 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. <section class="text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&:has([data-writing-block])>*]:pointer-events-auto [content-visibility:auto] supports-[content-visibility:auto]:[contain-intrinsic-size:auto_100lvh] R6Vx5W_threadScrollVars scroll-mb-[calc(var(--scroll-root-safe-area-inset-bottom,0px)+var(--thread-response-height))] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" data-turn-id="request-WEB:2c0f2ed6-be3b-4cc9-b6fa-40a93fbe4051-54" data-testid="conversation-turn-110" data-scroll-anchor="false" data-turn="assistant"><div class="text-base my-auto mx-auto pb-10 [--thread-content-margin:var(--thread-content-margin-xs,calc(var(--spacing)*4))] @w-sm/main:[--thread-content-margin:var(--thread-content-margin-sm,calc(var(--spacing)*6))] @w-lg/main:[--thread-content-margin:var(--thread-content-margin-lg,calc(var(--spacing)*16))] px-(--thread-content-margin)"><div class="[--thread-content-max-width:40rem] @w-lg/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn"><div class="flex max-w-full flex-col gap-4 grow"><div class="min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal outline-none keyboard-focused:focus-ring [.text-message+&]:mt-1" data-message-author-role="assistant" data-message-id="42c94d38-05e2-493e-9dc4-d3bce99b418b" dir="auto" data-message-model-slug="gpt-5-5-thinking" data-turn-start-message="true" tabindex="0"><div class="flex w-full flex-col gap-1 empty:hidden"><div class="markdown prose dark:prose-invert wrap-break-word w-full light markdown-new-styling">Collect and analyse empirical financial data on exchange-traded stocks, including stock returns and spreads, for economic prediction using time-series methods.</div></div></div></div></div></div></section> Introduction to Time SeriesUnivariate Time SeriesMultivariate Time SeriesGARCH ModelsHigh-Frequency Financial EconometricsApplications 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. 0 Other 100% Examination - 60%Group Coursework - 40% Marking and feedback available 15 working days 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 & FinanceBMAN71122 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 N Core TextPeter J. Brockwell & Richard A. Davis (2016), Introduction to Time Series and Forecasting, 3rd edition, SpringerTaylor, 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 textIn 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. Lectures 30 Practical classes & workshops 30 0 90