Modelling and Forecasting Financial Time Series ECON5022
- Academic Session: 2020-21
- School: Adam Smith Business School
- Credits: 20
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
The course offers an introduction to modelling and forecasting financial time series. The first part of the course will be mainly devoted to analysing univariate models for the conditional mean and the conditional variance (ARMA and GARCH models). These models will be used to produce forecasts. Additional topics, e.g. multiple time series analysis and nonlinear models may be discussed, if time allows. In the second part of the course will discuss forecasts evaluation, aimed to monitor and improve forecast performances. The course will be complemented by practical session using statistical or econometric software.
Two-hours lecture per week, for 10 weeks. Two-hours labs and/or tutorial will run for 7 weeks.
Requirements of Entry
Please refer to the current postgraduate prospectus at: http://www.gla.ac.uk/postgraduate/
Basic Econometrics (ECON5002)
Written assignment. Students will be required to work in groups (25% of final grade for course)..
Two-hour end-of-course examination (75% of final grade for course).
Main Assessment In: April/May
The first aim of the course is to introduce the basic models widely used to analyse and forecast financial time series. The second aim is to evaluate the forecast produced using these models.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ Select and fit the appropriate model to analyse financial time series.
■ Derive the main properties of the models used to analyse and forecast financial time series.
■ Produce optimal forecasts for a given information set and forecast horizon.
■ Evaluate critically the forecasts.
Model and predict financial time series using statistical/econometric software
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.