Skip to main content
Unit of study_

QBUS6830: Financial Time Series and Forecasting

Time series and statistical modelling is a fundamental component of the theory and practice of modern financial asset pricing as well as financial risk measurement and management. Further, forecasting is a required component of financial and investment decision making. This unit provides an introduction to the time series models used for the analysis of data arising in financial markets. It then considers methods for forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of financial return data; the Capital Asset Pricing Model (CAPM); financial return factor models, with known and unknown factors, in panel data settings; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting market risk measures such as Value at Risk. Emphasis is placed on applications involving the analysis of many real market datasets. Students are encouraged to undertake hands-on analysis using an appropriate computing package.

Code QBUS6830
Academic unit Business Analytics
Credit points 6
ECMT5001 or QBUS5001
Assumed knowledge:
Basic knowledge of quantitative methods including statistics, basic probability theory, and introductory regression analysis

At the completion of this unit, you should be able to:

  • LO1. describe and summarise, with appropriate statistics, the empirical properties of financial prices and returns data
  • LO2. design and estimate of a range of quantitative, statistical models used by financial analysts and forecasters
  • LO3. appraise the suitability of both models and methods of forecasting financial data, financial quantities, and outcomes
  • LO4. develop complex programs in Python software for estimation of financial time series models and forecasting.