Applied Time Series and Forecasting provides students with a robust statistical framework for assessing the behaviour of time series, such as asset prices. The course provides a balance of theoretical, computational and empirical concepts. There is equal number of lectures and computer labs, in order to ensure the students get hands-on experience with the stylized facts of various financial datasets. 

One two-hour lecture per week for 10 weeks.

One two-hour computer lab per week for 10 weeks.

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Assessment

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.

Normally, the group-based assessment listed above cannot be reassessed.

The main aim of this course is to provide students with all the tools necessary for the analysis of past time series data, in order to be able to forecast the future and help make financial decisions under uncertainty. The lectures teach how to define appropriate advanced statistical models for temporal dependence in financial data, as well as examining the relevant theory. The labs focus on advanced estimation and simulation techniques, and at the same time they allow students to gain hands-on experience with the stylized facts and time properties of different financial datasets (exchange rates, yield curve modelling, inflation, stock prices, oil prices etc).

By the end of this course students will be able to:

1. Devise the theoretical setting as well as the empirical stylized facts that pertain to different financial datasets.

2. Build appropriate statistical models and techniques for modelling such datasets.

3. Programme advanced algorithms in MATLAB that allow estimation and statistical inference.

4. Evaluate statistical estimates, forecasts and other time-series projections, in order to make financial decisions under uncertainty.

5. Collaborate effectively within a group work environment.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.