Module code: ECOM031

Module Overview

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

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

Workshop Hours: 11

Independent Learning Hours: 95

Lecture Hours: 11

Guided Learning: 22

Captured Content: 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

  • 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
Coursework COURSEWORK 1 30
Coursework COURSEWORK 2 70

Alternative Assessment


Assessment Strategy

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

  • Knowledge of different econometric techniques used  to model financial time series data series

  • The practical ability to build and assess appropriate econometric models using actual time series data

  • Ability to conduct independent research in the field of financial time series analysis, using rigorous statistical techniques

  • Ability in applying the knowledge acquired to real world financial and economic phenomena


Thus, the summative assessment for this module consists of:

  • A first individual assignment (worth 30% of the overall final mark) - connected with learning outcomes 1,2,4. A final individual assignment (worth 70% of the overall final mark) - connected with learning outcomes 1,3,4.

  • Both assessments give the chance to students to apply and showcase understanding of the theory studied in the lectures and understanding of the working of the software. These assessments typically involve conducting independent research on a selection of topics as part of the curriculum. The overall process typically includes gathering, analyzing and writing up the conclusions of the research project.


Formative assessment and feedback

Workshops to provide verbal feedback on the exercises which involve the use of software to build models of financial data series and help prepare students for the coursework. Office hours offer further feedback opportunities for students.



Module aims

  • To 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 time series data. This module also equips students with the necessary analytical, statistical and software skills required to successfully undertake the writing of the MSc dissertation as part of the module ECOM027 Research Methods.

Learning outcomes

Attributes Developed
001 Understand and critically evaluate models of asset return processes KC EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE
002 Create models of ARCH and GARCH processes using appropriate software KCPT DIGITAL SKILLS; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE
003 Analyse and create dynamic models with changing regimes using appropriate software KCPT DIGITAL SKILLS; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE
004 Estimate and test models of financial data using appropriate computer software KPT DIGITAL SKILLS; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The lectures provide an understanding of a variety of econometric techniques used in modelling the characteristic features of financial data series. The workshops  give experience in applying appropriate software to estimate and critically assess different models of financial data series. Students benefit from the “hybrid system” (lectures + workshops) due to the implicit “learning by doing” approach which enforces an important level of applicability and relevancy of the topics studied.  

 The learning and teaching methods include:

  • Lectures aimed at introducing the theoretical background of the studied topics; these are recorded and disseminated to students as captured content

  • Workshops aimed at applying the theory to real world scenarios

  • Guided learning aimed at deepening the understanding of the subject and complementing the material seen in class

  • Students are expected to actively engage both in the lectures and seminars. In particular, students are expected to bring their laptop and actively solve the econometric exercises provided in the workshops.

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

Other information

In line with the University's curriculum framework, the School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Digital capabilities

Students work in econometric software , collecting data and building econometric models

Resourcefulness & Resilience

Assessments feature real-world problem-based tasks and scenarios whilst also promoting students self-assessment of acquired knowledge in the subject.


Case studies and applications of econometric techniques as part of the toolkit of a professional economics / finance practitioner


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics (Econometrics and Big Data) MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
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

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 2025/6 academic year.