Financial Mathematics

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 is to provide students with an understanding of the main models used for macroeconomic policy formulation at the graduate level.

The aim of this module is to provide students with a solid foundation of the econometric techniques and skills that form the basis for the quantitative/econometric modules of their master's course. The module aims to provide students, through lectures and computer lab workshops, with the practical techniques economists often use to handle, analyse and interpret economic data.

The aim of this module is:

• to introduce the basic concepts of programming on the practical level;
• to introduce, explain, and implement numerical methods for solving ordinary and partial differential equations of industrial importance.

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.

Studying data science techniques for economic predictions is crucial in today's data-driven world, as it enhances forecasting accuracy and informs economic policy and strategy. The aim of this module is to equip students with the econometric skills necessary to interpret and leverage vast amounts of economic data to make reliable predictions. We will study the logic of predictive data analysis and the most widely used methods in contemporary economic literature.

This module aims to allow students to engage with and study issued related to the mechanics of currency markets, international finance and economics. It aims to use economic principles to examine international monetary arrangements and important historical policy episodes.

The aim of this module is

• To introduce both supervised and unsupervised methods for learning from data.
• To introduce methods of dimensionality reduction.
• To introduce the R statistical programming language for implementing methods using real data.

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.

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.