Statistics with Applications to Finance MSc

You will study 180 or 185 credits in total during your Statistics with Applications to Finance MSc. A standard module is typically worth 15 credits and the research project is worth 60 credits. These are the modules studied in 2019. If you are starting in September 2020, these will give you a flavour of the modules you are likely to study. All Modules are subject to change.

Compulsory modules

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. 

Statistical Computing - 15 credits 

The use of computers in mathematics and statistics has opened up a wide range of tech- niques for studying otherwise intractable problems and for analysing very large data sets."Statistical computing" is the branch of mathematics which concerns these techniques for situations which either directly involve randomness, or where randomness is used as part of a mathematical model.

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 and Robustness - 15 credits

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. 

The full list of optional modules can be read in the course catalogue.