Financial Econometrics - MA886

Location Term Level Credits (ECTS) Current Convenor 2019-20
(version 5)
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7 15 (7.5) DR S Hadjiantoni


Pre-requisite: MAST8820: Advanced Regression Modelling, MAST8810 Probability and Classical Inference





Stationary Time Series: Stationarity, Autocovariance and autocorrelation functions, Partial autocorrelation functions, ARMA processes. ARIMA Model Building and Testing: Estimation, Box Jenkins, Criteria for choosing between models, Diagnostic tests for the residuals of a time series after estimation. Forecasting: Holt-Winters, Box-Jenkins, Prediction bounds. Distributional properties of asset returns, Regression test for CAPM, Multifactor models, Financial applications of AR, MA, and ARMA, Predicting asset returns, ARCH and GARCH models, Random walk hypothesis tests, Volatility processes.


This module appears in:

Contact hours


Method of assessment

80% examination and 20% coursework

Indicative reading

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-Verlag.
Ruey S. Tsay (2002). Analysis of financial time series, Wiley.
J.Y. Campbell, A.W. Lo and A.C. Mackinlay (1997). The Econometrics of Financial Markets, Princeton University Press, New Jersey.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:

1 will have a good understanding of processes underlying time series data;
2 will be able to apply with some theoretical justification the time series techniques encountered in various applications;
3 will understand financial time series and their characteristics;
4 will be able to test capital asset pricing model (CAPM);
5 will be able to carry out some time series analysis in finance.

The intended generic learning outcomes. On successfully completing the module students will be able to:

1 will have further developed a logical, mathematical approach to solving problems;
2 will have enhanced their ability to work with relatively little guidance;
3 will be able to use information technology for data retrieval, data analysis and presentation;
4 will have gained further organisational and interpersonal skills;
5 will have improved their key skills in written communication, numeracy and problem solving.

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