The aims of this module are to:

• Provide a mathematical understanding of the Lebesgue measure and integration.
• Generalise concepts to abstract measure spaces.
• Build a solid rigorous mathematical background for students to proceed to stochastic analysis and financial mathematics.

The aim of this module is to:

• To provide students with a rigorous mathematical introduction to the modern financial theory of security markets in discrete and continuous time models.
• To give students a solid theoretical background in the derivatives industry in discrete and continuous time models.

The aim of this module are to:

• provide students with a solid foundation in those mathematical and statistical techniques and skills used both in their other MSc modules and in the financial industry.
• introduce students to software such as Python and Excel widely used in the financial industry.

The aims of this module are to:

• provide an overview of global financial markets and institutions, including: an appreciation of the economic problems that financial institutions and markets seek to overcome
• understand the organisation and operation of the major global financial markets and financial institution
• be familiar with the business models financial firms and insights into the impact of technology and regulation on the evolution of the industry
• be able to conduct further analysis of financial markets and institutions, building on the foundation provided by the module

The aim of this module is to introduce, explain, and implement numerical methods for solving partial differential equations.

This module introduces students to the emerging field of data science and equips them with the fundamental knowledge of using data to gain insights and support decision-making.

The module demonstrates and provides hands-on experience with cleaning, integrating, exploring, transforming and summarising data sets.

It teaches students to form questions and hypotheses from data; to utilise and apply a variety of statistical methods to effectively analyse data in a way that answers those questions or hypotheses; and to create suitable visualisations to communicate their analyses. By the end of the module students will in analysing and presenting data using R and RStudio.

This module aims to:

• Introduce numerical methods and associated theory for modelling of financial options.
• Teach students how to implement such numerical methods on computers.
• Gain experience in interpreting numerical results.

The aim of this module is to introduce students to:

• The basics of stochastic calculus by using Brownian motion as an integrator.
• Mathematical modelling of pricing via the Black-Scholes model.

The aims of this module are to:

• develop comprehensive big data analytical skills for real-life problem-solving in accounting and finance;
• explore unstructured data and its application in accounting and finance;
• foster openness to new ideas and awareness of alternative solutions which should be evaluated;
• develop relevant transferable skills.

The aims of this module are to gain familiarity with modern qualitative theory of linear PDE's with particular emphasis on second-order equations as well as to study selected aspects of modern methods for simple nonlinear PDEs.

The aim of this module is to gain familiarity with theory and techniques of static optimisation and dynamic optimisation.

This module introduces the use of statistical models for data summary and prediction using the R or Python programming language and R packages.

The aim of this module is to give the students experience of independent work in an area of mathematics with relevance or applications to the field of finance.