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 three aspects around the use of computational processing in Finance:

The use of computation in the analysis of finance algorithmic instruments. This includes the use of time series analysis and algorithmic trading.
The computation and programming concepts behind cryptocurrencies.
The ethical and sustainability aspects of computational finance and crypto currencies.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

The mathematical tools, and their computational implementations, underlying the subject
The concepts underlying computational finance
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:

Implement computational solutions to real-world financial problems in portfolio optimization and time series analysis
Analyse the strength and limitations of Blockchain technologies
Describe the emerging variants of cryptocurrency-based decentralized system
Apply fundamental approaches for option pricing and portfolio optimisation

Learning and Teaching

Study time
Type	Hours
Wider reading or practice	20
Lecture	36
Preparation for scheduled sessions	24
Completion of assessment task	50
Total study time	130

Resources & Reading list

General Resources

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.

Textbooks

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

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

Andreas M. Antonopoulos (2014). Mastering Bitcoin: Unlocking Digital Cryptocurrencies. O'Reilly Media.

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

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

Tomas Cipra (2020). Time Series in Economics and Finance. Springer International Publishing.

(2019). Statistics of Financial Markets: An Introduction. Springer.

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Group Assessment	25%
Continuous Assessment	25%
Computer assisted assessment	50%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Computer assisted assessment	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Computer assisted assessment	100%

Repeat Information

Repeat type: Internal & External