Financial Mathematics MSc

Compulsory

• Probability and Stochastics

  This module aims to equip students with the basic measure-theoretic and probabilistic concepts and techniques needed for them to be able to apply the modern mathematical theory of finance, and to enable students to use methods of stochastic calculus based on Brownian motion in such a way that they are able to carry out the necessary mathematical manipulations and calculations required for use and critical assessment of the various financial models introduced in other modules of the programme. 

• Financial Markets

  This module aims to equip students with the basic concepts of financial markets, including market terminology and conventions, required for other modules of the programme, and to enable candidates to perform calculations to obtain solutions to basic portfolio optimisation problems using a range of models and techniques. 

• Option Pricing Theory

  This module aims to equip students with the notion of risk-neutral valuation and the relation between physical and risk-neutral probability measures in the Brownian context, and to enable students to price vanilla options and basic barrier options in the geometric Brownian motion model.

• Interest Rate Theory

  This module aims to equip students with a basic familiarity with fixed-income securities markets and, in particular, with the structures of key financial products traded in such markets, and to enable candidates to value derivatives in a number of basic models for interest rates and discount bonds.

• Research Methods and Case Studies

  The aims of this module are to develop students’ knowledge and critical awareness of a variety of research methods, to encourage students to develop critical thinking skills and transferable skills appropriate to their discipline, to enable students to develop an understanding of the current needs of industry and commerce, and to prepare students for their dissertation. 

• Advanced Computational Statistics for Data Analytics

  This module aims to introduce the students to a range of computational intensive statistical methods, to further develop their skills in correct interpretation and clear reporting of results, and to enable the students to create algorithms for regression models (parametric regression and nonparametric regression) to cope with massive data. 

• Financial Mathematics Dissertation

  The dissertation aims to enable students to develop a robust understanding of more advanced and practical issues in risk quantification or risk management, or other areas in financial engineering, and to enable students to stimulate independent learning and critical thinking, both as a means for studying the chosen project and for approaching other real-life applications.

• Quantitative Data Analysis and Visualisation

  The aim of this module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. Content covers a practical understanding of core methods in data science application and research, such as bivariate and multivariate methods, regression and graphical models. A focus is also placed on learning to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods. 

Optional

• Time Series Forecasting and Risk Modelling

  This module aims to equip students with the ability to employ different methods for modelling and forecasting time series data, in particular in the context of financial data and forecasting financial risk, and to enable students to apply a range of models and tools to make financial decisions such as risk assessment.

• Fundamentals of Machine Learning

  This module aims to equip students with the knowledge and ability to use modern regression and classification methods with different types of data, to enable students to apply a range of models and tools to variable selection and model selection.