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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. This module covers two aspects of the use of computational processing in Finance, • The use of computation in the analysis of finance algorithmic instruments. This includes the use of statistical analysis and machine learning. • The computation and programming concepts behind cryptocurrencies.

Aims and Objectives

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
  • The concepts underlying Cryptocurrencies.
  • Theoretical foundation of Blockchain technologies
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Analyse the strength and limitations of Blockchain technologies
  • Describe the emerging variants of cryptocurrency-based decentralized system
  • Identify the methods required to analyse information from financial data and trading systems.
  • Explain the approaches required to calculate the price of options.


Part I: Data-driven models a. Financial Instruments b. Portfolio optimisation c. Introduction to stochastic processes and the pricing of derivatives d. Foundations of time series analysis Part II: Foundations of Blockchain a. Concepts underpinning Cryptocurrencies b. Consensus in decentralized systems c. Bitcoin mining d. Platforms, tokens and Smart contracts

Learning and Teaching

Wider reading or practice20
Preparation for scheduled sessions74
Completion of assessment task20
Total study time150

Resources & Reading list

J. C. Hull (2017). Options, Futures and other Derivatives. 

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

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

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

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

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.



MethodPercentage contribution
Continuous Assessment 25%
Final Assessment  75%


MethodPercentage contribution
Set Task 100%


MethodPercentage contribution
Set Task 100%

Repeat Information

Repeat type: Internal & External

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