Module code: ECOM031

Module Overview

Introduction to modern econometric techniques used in the analysis of financial time series. Topics include ARIMA models, ARCH & GARCH and Stochastic Volatility models, estimating and testing the CAPM, fractional integration and nonlinear models.

Module provider


Module Leader

RISPOLI Luciano (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 128

Lecture Hours: 22

Seminar Hours: 11

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative content includes:

  • Asset return processes: stationarity, random walks, tests for unit roots

  • Conditional volatility: ARCH, GARCH, GARCH-M and E-GARCH models

  • Stochastic volatility models

  • Estimating and testing the Capital Asset Pricing Model

  • Long term memory and fractional integration in stock market returns

  • Non-linear models including SETAR, STAR, Markov models of regime switching

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test Class test (1 hour) 25
Examination EXAMINATION - 2 HOURS 75

Alternative Assessment

Not applicable

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate

  • knowledge of different econometric techniques used with financial data series

  • the practical ability to build and assess appropriate econometric models using actual data

Thus, the summative assessment for this module consists of:

  • A class test (1 hour) in week 8

  • A two-hour examination covering the econometric techniques discussed in the lectures, scheduled in weeks 13-15.

Formative assessment and feedback

Classes to provide verbal feedback on the exercises which teach the use of software to build models of financial data series and help prepare students for the coursework.

Module aims

  • examine a variety of econometric techniques developed to analyse financial data series and use appropriate software to apply these techniques to estimate models of actual financial data 

Learning outcomes

Attributes Developed
1 Understand and critically evaluate models of asset return processes C
2 Build models of ARCH and GARCH processes using appropriate software PT
3 Analyse dynamic models with changing regimes C
4 Estimate and test models of financial data using appropriate computer software PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

The lectures provide an understanding of a variety of econometric techniques used in modelling the characteristic features of financial data series. The classes give experience in applying appropriate software to estimate and critically assess different models of financial data series.

The learning and teaching methods include:

  • 1 hour lecture per week x 11 weeks

  • 1 hour class per week x 11 weeks

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: ECOM031

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
Economics and Finance MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
Business Economics and Finance MSc 2 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.