This module is not currently running in 2024 to 2025.
Overview of statistical methods. Stationary time series. Autocovariance and autocorrelation functions. Partial autocorrelation functions. ARMA processes. ARIMA model building, testing and estimation. Criteria for choosing between models. Forecasting. Cointegration. Prediction bounds. Asset return and risk. Term structure of interest rates. Distributional properties of asset returns. Testing for CAPM. Testing random walk hypothesis and predicting asset return. Sharpe ratio and efficient portfolio. Cross-section modelling and GMM. Estimate multifactor models. Financial applications of AR, MA, and ARMA. ARCH and GARCH models. Volatility processes. Simple applications of these techniques using R.
In addition, level 7 students will study advanced applications of these techniques using R.
Total contact hours: 40
Private study hours: 110
Total study hours: 150
Level 6 module:
Assessment 1 (10-15 hrs) 20%
Assessment 2 (10-15 hrs) 20%
Examination (2 hours) 60%
Level 7 module:
Assessment 1 (10-15 hrs) 20%
Assessment 2 (10-15 hrs) 20%
Examination (2 hours) 60%
Reassessment methods
Like-for-like
Enders, W. (2004). Applied Econometric Time Series. New York: Wiley.
Brockwell, P.J. & Davis, R.A. (2002). Introduction to Time Series and Forecasting. New York: Springer.
Ruey S. Tsay (2002). Analysis of financial time series, New York: Wiley
Campbell, J.Y., Lo, A.W. and Mackinlay, A.C. (1997). The Econometrics of Financial Markets, New Jersey: Princeton University Press.
Lyuu Y. (2002). Financial Engineering and Computation. Cambridge University Press
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes. On successfully completing the module students will be able to:
1. demonstrate systematic understanding of financial time series data analysis;
2. demonstrate the capability to solve complex problems using a very good level of skill in calculation and manipulation of the material in the following areas: ARIMA and
GARCH model building, testing and estimation, model selection, forecasting, financial hypothesis testing and modelling in the context of asset returns, the efficient
portfolio;
3. apply a range of concepts and principles in financial time series data analysis in loosely defined contexts, showing good judgement in the selection and application of
tools and techniques;
4. make effective and well-considered use of R.
The intended generic learning outcomes. On successfully completing the module students will be able to:
1 manage their own learning and make use of appropriate resources;
2 understand logical arguments, identifying the assumptions made and the conclusions drawn;
3 communicate straightforward arguments and conclusions reasonably accurately and clearly and communicate technical material competently;
4 manage their time and use their organisational skills to plan and implement efficient and effective modes of working;
5 solve problems relating to qualitative and quantitative information;
6 make competent use of information technology skills such as online resources (mMoodle);
7 communicate technical and non-technical material competently;
8 demonstrate an increased level of skill in numeracy and computation;
9 demonstrate the acquisition of the study skills needed for continuing professional development.
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