Advanced Time Series Modelling

Module overview

The module offers a comprehensive introduction to Advanced Time Series Modelling. You will learn various analytical tools to enable you to analyse financial data. The module expects prior skills in data analysis covered by the module Quantitative Finance (MANG6299) in the 1st semester. In addition, Students on the MSc Risk and Finance can choose this module but they will need to have taken MANG6003 Quantitative Methods in the first semester if they wish to do so.

Aims and Objectives

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

Develop quantitative models.
Analyse financial data;

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Critically evaluate statistical models and forecasting tools
Evaluate model fit;
Assess out-of-sample properties;
In-depth analysis of financial data
Relate forecasts to strategic decisions

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Forecasting of financial time series;
Econometric modelling of time series data
Competence in using an econometric software package

Learning and Teaching

Teaching and learning methods

Teaching methods include The module will be taught by a mixture of methods ranging from guided background reading, lectures, group work and the exploration of mini case studies and datasets. The lecturer will draw upon market developments current at the time of the course. The lecturer will introduce the concepts, and you will have the opportunity to practice and apply the methods discussed. We will do a step-by-step analysis of different financial data (stock market data, firm and industry-specific data). Learning activities include •EVIEWS or STATA based exercises in class •A group assignment •Discussion of findings in class There will be many opportunities for you to gain feedback from your tutor and/or peers about your level of understanding and knowledge prior to any formal summative assessment such as coursework or examinations. In particular, class exercises and short presentations in class will provide an opportunity for feedback from peers and tutors.

Study time
Type	Hours
Independent Study	126
Teaching	24
Total study time	150

Resources & Reading list

Textbooks

Wooldridge, J.M. (2009). Introductory Econometrics. South-Western.

Asterious, G. and S. Hall (2011). Applied Econometrics. Palgrave Mcmillan.

Greene, W.H. (2000). Econometric Analysis. New York: Prentice Hall International Inc..

Chiang, A.C. (1984). Fundamental Methods of Mathematical Economics. Singapore: McGraw-Hill.

Campbell, J.Y., Lo, A.W. & A.C. MacKinlay (1997). The Econometrics of Financial Markets. Princeton: Princeton University Press.

Hayashi, F. (2000). Econometrics. Princeton: Princeton University Press.

Enders, W. (2014). Applied Econometric Time Series. Wiley.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Computer practicals

Assessment Type: Formative
Feedback: Students will get feedback in terms of their success in completing the computer labs following each lecture. Their ability to complete the set tasks will provide feedback on their progress and flag any issues.
Final Assessment: No
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Group presentation	20%
Examination	80%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat Information

Repeat type: Internal & External