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.