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MATH1167 Techniques of Calculus and Linear Algebra. STAT1041 Statistical Data Analysis and Time Series.

The aim of this module is to investigate the mathematical foundations of modern financial data analysis with the analysis of asset price time series. The module also studies modern price volatility models and their practical application in assessing risk. This module aims to enhance graduates’ employability by giving students practical experience of using statistical models in the financial analysis of asset prices.

On successful completion of this course a student will be able to:
1. Make use of the concepts of asset price returns and their volatility, and their use in measuring risk.
2. Describe and use auto-covariance, auto-correlations and partial auto-correlations and relevant statistical tests. Distinguish stationary and non-stationary behaviour in time series. Apply hypothesis testing in finance.
3. Describe and apply random walk models to financial time series and apply the maximum likelihood method to fitting ARCH and GARCH models to realistic data and use the models for forecasting asset price volatility.
4. Use Excel & R to analyse realistic financial data sets, interpret outputs and report on findings.

Fundamental concepts on Financial Time Series.
Stationary and non-stationary time series models.
Forecasting, model identification, parameter estimation and model selection for the time series models.
Unit-root test and random walk model.
Non-linear models: ARCH and GARCH models.
Managing risk in practice.

Lecture: Concepts, methods and basic conclusions will be introduced and explained in lectures. Problem solving and technique training will be done through tutorials.
Self-directed learning: Question sheets, tutorial activities and signposting to learning resources.

Coursework: 100% weighting, 40% pass mark.
Learning Outcomes: 1 - 4.
Outline Details: Questions involving the application of financial times series.

Formative Assessment: Weekly worksheets.