FINANCIAL ECONOMETRICS - 2021/2
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
Economics
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: 106
Lecture Hours: 11
Captured Content: 22
Module Availability
Semester 2
Prerequisites / Co-requisites
None
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 |
|---|---|---|
| Coursework | COURSEWORK 1 | 30 |
| Coursework | COURSEWORK 2 | 70 |
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
https://readinglists.surrey.ac.uk
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 and Finance 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 |
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 2021/2 academic year.