Time Series Analysis and Financial Forecasting
|Unit level:||Level 4|
|Teaching period(s):||Semester 2|
|Offered by||School of Mathematics|
|Available as a free choice unit?:||N
- MATH20701 - Probability 2 (Compulsory)
- MATH20802 - Statistical Methods (Compulsory)
Additional RequirementsMATH48032 pre-requisites
Students are not permitted to take more than one of MATH38032 or MATH48032 for credit in the same or different undergraduate year. Students are not permitted to take MATH48032 and MATH68032 for credit in an undergraduate programme and then a postgraduate programme.
To introduce the basic concepts of the analysis of time series in the time domain and to provide the students with experience in analysing time series data.
This course unit covers a variety of concepts and models useful for empirical analysis of time series data.
Teaching and learning methods
Three lectures and one axamples class each week. In addition students should expect to spend at least six hours each week on private study for this course unit.
On successful completion of this course unit students wil be able to:
- Explain the concepts and general properties of stationary and integrated univariate time series.
- Explain the concepts of linear filter and linear prediction, and derive best linear predictors for time series.
- Apply the backwards shift operator and the concept of roots of the characteristic equation to the study of time series models.
- Explain the concepts of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and seasonal autoregressive integrated moving average (seasonal ARIMA) time series, and derive basic properties thereof.
- Apply the basic methodology of identification, estimation, diagnostic checking and model selection to time series model building.
- Explain some basic concepts in the analysis of multivariate time series - multivariate autoregressive model, joint stationarity and cointegration.
- Explain the concept of heteroscedasticity and derive basic properties of generalised autoregressive conditionally heteroscedastic (GARCH) models.
- Apply principles of statistical inference to evaluate GARCH-like and ARMA-GARCH models fitted to time series, forecast volatility, and infer the presence or absence of properties like leverage and asymmetry in the underlying financial assets.
- Other - 20%
- Written exam - 80%
Assessment Further Information
- Coursework: homework assignment weighting 20%.
- End of semester examination: three hours weighting 80%
- Introduction and examples of economic and financial time series, asset returns. Basic models: white noise, random walk, AR(1), MA(1). 
- Stationary time series. Autocovariance and autocorrelation functions. Linear Prediction. Yule-Walker equations. Estimation of autocorrelation and partial autocorrelation functions. 
- Models for stationary time series - autoregressive (AR) models, moving average (MA) models, autoregressive moving average (ARMA) models. Seasonal ARMA models. Properties, estimation and model building. Diagnostic checking. 
- Non-stationary time series. Non-stationarity in variance - logarithmic and power transformations. Non-stationarity in mean. Determinisitic trends. Integrated time series. ARIMA and seasonal ARIMA models. Modelling seasonality and trend with ARIMA models. 
- Filtering, exponential smoothing, seasonal adjustments. 
- Multivariate time series. Stationarity, autocorrelation and crosscorrelation. Multivariate autoregressive model. Markov property. Representation of univariate autoregressive models in Markov form. 
- Model based forecasting, from ARMA and ARIMA. 
- Conditionally heteroskedastic models â€' ARCH-type models. Volatility forecasting. 
- Cointegration. 
- Examples of non-linear models - threshold AR, bilinear models, regime switching models. 
- Cryer, Jonathan D and Chan, Kung-Sik. Time Series Analysis with Applications in R. Second edition. Springer, 2008.
- Mills, Terence C. The Econometric Modelling of Financial Time Series. Second edition. Cambridge University Press, 1999.
- Shumway, Robert H and Stoffer, David S. Time Series Analysis and Its Application: With R Examples. Second edition. Springer, 2006.
- Cowpertwait, Paul SP and Metcalfe, Andrew V. Introductory Time Series with R. Springer, 2009.
Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding. Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback. Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.
- Lectures - 33 hours
- Tutorials - 11 hours
- Independent study hours - 106 hours