This module aims to provide a comprehensive and systematic introduction to financial time series models and their applications to modelling and prediction of financial time series data.
Module learning outcomes
At the end of the module students should:
Know the basic characteristics of financial data, understand the application of financial time series models, and gain the experience in analysing financial time series.
Have a reasonable ability to derive theoretical results relating to some important financial time series models.
Have a reasonable ability to fit time series models to time series data, and carry out related predictions using appropriate computer software.
Have a reasonable ability to use residual plots and other techniques to assess the goodness of fit of a time series model.
Have a reasonable ability to choose between alternative time series models for sets of time series data.
Assessment
Task
Length
% of module mark
Closed/in-person Exam (Centrally scheduled) Financial Time Series
2 hours
100
Special assessment rules
None
Reassessment
Task
Length
% of module mark
Closed/in-person Exam (Centrally scheduled) Financial Time Series
2 hours
100
Module feedback
Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.
Indicative reading
Ruey S. Tsay, Analysis of Financial Time Series, Wiley (2010).
Fan, J. and Yao, Q. Nonlinear Time Series: Nonparametric and Parametric Methods, Springer-Verlag, New York (2003)