Advanced Time Series Modelling

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

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

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Develop quantitative models.
• Analyse financial data;

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
• Econometric modelling of time series data
• Forecasting of financial time series;

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

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.

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.

Referral

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

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.