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
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Apply fundamental approaches for option pricing and portfolio optimisation
- 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
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The concepts underlying Cryptocurrencies.
- The mathematical tools, and their computational implementations, underlying the subject
- Theoretical foundation of Blockchain technologies
- The concepts underlying computational finance
Part I: Data-driven models
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
In addition, the course will cover ethical and sustainability aspects around computational finance, e.g. the use of algorithmic trading and crypto currencies.
Learning and Teaching
|Wider reading or practice||20|
|Completion of assessment task||50|
|Preparation for scheduled sessions||24|
|Total study time||130|
Resources & Reading list
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.
J. C. Hull (2017). Options, Futures and other Derivatives. Pearson.
Tomas Cipra (2020). Time Series in Economics and Finance. Springer International Publishing.
(2019). Statistics of Financial Markets: An Introduction. Springer.
P. Wilmott, Paul Wilmott (2007). Paul Wilmott Introduces Quantitative Finance. Wiley.
Andreas M. Antonopoulos (2014). Mastering Bitcoin: Unlocking Digital Cryptocurrencies. O'Reilly Media.
P. Brandimarte (2006). Numerical methods in finance and economics. Wiley.
Satoshi Nakamoto (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
This is how we’ll formally assess what you have learned in this module.
|Computer assisted assessment||50%|
This is how we’ll assess you if you don’t meet the criteria to pass this module.
|Computer assisted assessment||100%|
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.
|Computer assisted assessment||100%|
Repeat type: Internal & External