The University of Southampton

COMP6212 Computational Finance

Module Overview

Despite the current financial climate, the subject of Computational Finance is both intellectually challenging and attractive to students from an employment point of view. It is thought the MSc in Artificial Intelligence could be made more attractive to overseas students by the introduction of this topic in the curriculum. Also, many graduates from our undergraduate programmes join the financial sector, and are likely to find knowledge of the subject giving them a competitive advantage when attempting to do so. Elsewhere in the University there are modules and programmes in Mathematical Finance, and this module will be designed to be distinct from them in emphasising the computational aspect, in a hands-on teaching environment. To do this module, you should be competent in basic calculus, including ordinary differential equations, and programming in some high level language (e.g. Java)

Aims and Objectives

Module Aims

To provide an overview of computational finance

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The concepts underlying computational finance
  • The mathematical tools, and their computational implementations, underlying the subject
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Implement a simulated fund management system that uses real-life data from the stock exchange


Mathematical preliminaries - Numerical analysis - Optimisation - Stochastic differential equations - Monte-Carlo simulations Software preliminaries - MATLAB - Finance toolbox in MATLAB - Other tools - overview of R and packages Financial instruments and their uses Portfolio optimisation - Utility theory - Quantifying risk Options pricing - Black-Scholes model - Options pricing by Monte Carlo methods

Learning and Teaching

Follow-up work12
Wider reading or practice10
Completion of assessment task80
Preparation for scheduled sessions12
Total study time150

Resources & Reading list

P. Wilmott, Paul Wilmott (2007). Introduces Quantitative Finance. 

Teaching space, layout and equipment required. A terminal room with a data projector, so that some explanation of the material can be done in class, followed by students working on computerbased exercises, with 6 two hour sessions to be timetabled in this environment; Access to MATLAB Finance Toolbox as many simultaneous licenses as there are students in the class; Copies of course text purchased for the library, made available for long term borrowing; the module will closely follow this book, so as many copies as there are students registered would be ideal; Several copies of the two additional reading books available in the library for short term borrowing; The module will have in-class computer-based teaching and assessment, and associated software license requirements; hence the maximum class size should be 25.

P. Brandimarte (2006). Numerical methods in finance and economics. 

J. C. Hull (2009). Options, Futures and other Derivatives. 



MethodPercentage contribution
Laboratory 100%


MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Prerequisites: MATH2047 and ELEC2204 (or COMP2210)

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