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The University of Southampton

COMP6212 Computational Finance

Module Overview

Financial markets form the source of a vast number of challenging computational problems. These are not only intellectually challenging from the point of view of computational modelling, but the financial sector is also an employer of a significant fraction of graduates of Computer Science, Software Engineering, Artificial Intelligence and Data Science. The subject may be approached at various levels. At one end, there are the details of financial instruments and the regulatory frameworks under which they are traded, which are usually taught in Business Schools. At the more theoretical end, Mathematical Finance covers the calculus of stochastic systems, and is usually taught in academic departments of Pure Mathematics. Here, we take an approach that is computational, emphasising the algorithmic aspects of finance, and discuss the challenges involved in deploying computational algorithms to extract useful information from financial data and to model the behaviour of some complex financial instruments and systems. Self-study material will be provided for students who have not taken COMP3206 or COMP6229 Machine Learning.

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
  • Theoretical foundation of Blockchain technologies
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Implement and analyse computational algorithms that are useful in extracting useful information from financial data and trading systems, using historic financial data on prices of assets, derivatives and other determinants of macroeconomics.
  • Analyse the strength and limitations of Blockchain technologies.
  • Investigate emerging variants of cryptocurrency-based decentralized systems.


Part I: Data-driven models a. Mathematical Preliminaries b. Foundations of time series analysis c. Portfolio optimisation d. Introduction to stochastic processes and the pricing of derivatives Part II: Foundations of Blockchain a. Introduction to Crypto and Cryptocurrencies b. Consensus in decentralized systems c. Bitcoin mining d. Incentives, scalabilities, transactions, variations

Learning and Teaching

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

Resources & Reading list

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

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

Satoshi Nakamoto (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. 

Teaching space, layout and equipment required. Teaching will be in standard lecture rooms and time-tabled laboratory sessions in which students will need access to individual desktop computers running MATLAB and its Financial Toolbox. The introductory part will use several illustrations using this toolbox, but the second part of the module may be implemented in any programming language of convenience in discussion with the instructor. Due to time-tabled laboratory supervisions of advanced material, the number of students registered on this module may be capped according to lab capacity.

Andreas M. Antonopoulos (2014). Mastering Bitcoin: Unlocking Digital Cryptocurrencies. 

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



MethodPercentage contribution
Coursework assignment(s) 50%
Coursework assignment(s) 50%


MethodPercentage contribution
Coursework assignment(s) 100%


MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

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