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
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Critically evaluate statistical models and forecasting tools
- Relate forecasts to strategic decisions
- In-depth analysis of financial data
- Assess out-of-sample properties;
- Evaluate model fit;
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Analyse financial data;
- Develop quantitative models.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Competence in using an econometric software package
- Forecasting of financial time series;
- Econometric modelling of time series data
Syllabus
The module will introduce methods developed in time series analysis and apply these methods to financial data. The module will stress the relationship between finance, econometrics and statistics. The module will be also offered as an option on other programmes (i.e. MSc International Financial Markets, MSc International Banking and Financial Studies). The module will use comparative case studies and will analyse financial data in different settings (countries, industries and governance mechanism).
Topics:
Classical time series analysis
- Deterministic trends
- Cyclicality
- Seasonality
ARIMA models
- Box-Jenkins approach
- Forecasting
- Out-of-sample properties
Structural breaks
- Testing for structural breaks
- Endogenous and exogenous tests
Vector autoregression (VAR)
- Short-term dynamics
- Lag specification
- Forecasting
Co-integration and long-term equilibrium
- Johansen procedure
- Structural breaks in long-term equilibrium
Vector error correction models (VECM)
- Speed of adjustment
- Short and long-term dynamics
Modelling conditional volatility
- ARCH model
- GARCH model
Multivariate time series modelling
- Panel vector autoregression
- Panel co-integration
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.
Type | Hours |
---|---|
Teaching | 24 |
Independent Study | 126 |
Total study time | 150 |
Resources & Reading list
Textbooks
Campbell, J.Y., Lo, A.W. & A.C. MacKinlay (1997). The Econometrics of Financial Markets. Princeton: Princeton University Press.
Enders, W. (2014). Applied Econometric Time Series. Wiley.
Wooldridge, J.M. (2009). Introductory Econometrics. South-Western.
Hayashi, F. (2000). Econometrics. Princeton: Princeton University Press.
Chiang, A.C. (1984). Fundamental Methods of Mathematical Economics. Singapore: McGraw-Hill.
Greene, W.H. (2000). Econometric Analysis. New York: Prentice Hall International Inc..
Asterious, G. and S. Hall (2011). Applied Econometrics. Palgrave Mcmillan.
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.
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.
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.
Method | Percentage contribution |
---|---|
Examination | 100% |
Repeat Information
Repeat type: Internal & External