You will study 180 or 185 credits in total during your Statistics with Applications to Finance MSc. These are the modules studied in 2021/22 and will give you a flavour of the modules you are likely to study in 2022/23. All modules are subject to change.
Dissertation in Statistics - 60 credits
Each student will discuss with an individual supervisor a suitable research project. The title and objectives of the project will be approved by the Programme Manager.
Discrete Time Finance - 15 credits
The aim of this module is to develop a general methodology for the pricing of financial assets in risky financial markets based on discrete-time models.
Continuous Time Finance - 15 credits
This module develops a general methodology for the pricing of financial assets in risky financial markets based on continuous-time models.
Risk Management - 15 credits
This module covers the different sorts of risk to which financial investments are exposed, basic and sophisticated derivates commonly used for hedging, expected utility theory, models of incomplete markets, Value-at-Risk and other risk measures, credit risks and credit derivatives, methods to determine the effectiveness of a hedge, stress-testing of risky investment portfolios.
Time Series and Spectral Analysis - 15 credits
The module will concentrate on techniques for model identification, parameter estimation, diagnostic checking and forecasting within the autoregressive moving average family of models and their extensions.
Optional modules include
Independent Learning and Skills Project - 15 credits
Students will be able to develop a systematic search strategy to find material on a given topic, using Mathematical word processing and evaluation of material, referencing conventions.
Linear Regression, Robustness and Smoothing - 20 credits
In many areas of science and social study, several variables or measurements are taken from each member of a sample. This module will examine ways of predicting one particular variable from the remaining measurements using the linear regression model. The general theory of linear regression models will be covered, including variable selection, tests and diagnostics. Robust methods will be introduced to deal with the presence of outliers. Nonparametric models will be introduced to relax the assumption of a linear relationship.