|Study location||United Kingdom, Colchester Campus|
|Type||Master courses, full-time|
|Nominal duration||1 year|
|Tuition fee||£14,500 per year|
Undergraduate diploma (or higher)
overall grade of 2:2 and above
The entry qualification documents are accepted in any language
IELTS: 6.0 overall (with a minimum component score of 5.5)
At least 1 reference(s) must be provided.
Reference should be written on official letterhead, signed and dated. Please upload it in the Documents section.
Our MSc Computational Finance equips you with the core concepts and mathematical principles of modern quantitative finance, plus the operational skills to use computational packages (mainly Matlab) for financial modelling.
We provide practical, hands-on learning about how modern, highly computerised financial markets work, how assets should be priced, and how investors should construct a portfolio of assets. In addition to traditional topics in derivatives and asset pricing, we place a special emphasis on risk management in non-Gaussian environment with extreme events.
You master these areas through studying topics including:
- Non-linear and evolutionary computational methods for derivatives pricing and portfolio management
- Applications of calculus and statistical methods
- Computational intelligence in finance and economics
- Financial markets
You also graduate with an understanding of the use of artificial financial market environments for stress testing, and the design of auctions and other financial contracts.
Our Centre for Computational Finance and Economic Agents is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation.
Our research is geared towards real-world, practical applications, and many of our academic staff have experience of applying their findings in industry and in advising the UK government.
CCFEA MSc Dissertation
Financial Engineering and Risk Management
Introduction to Financial Market Analysis
Learning and Computational Intelligence in Economics and Finance
Professional Practice and Research Methodology
Quantitative Methods in Finance and Trading
Big-Data for Computational Finance (optional)
Industry Expert Lectures in Finance (optional)
Mathematical Research Techniques Using Matlab (optional)
Programming in Python (optional)
Artificial Neural Networks (optional)
High Frequency Finance and Empirical Market Microstructure (optional)
Machine Learning and Data Mining (optional)
Trading Global Financial Markets (optional)
Creating and Growing a New Business Venture (optional)
Evolutionary Computation and Genetic Programming (optional)
Constraint Satisfaction for Decision Making (optional)
Our recent graduates have gone on to become quantitative analysts, portfolio managers and software engineers at various institutions, including:
Mitsubishi UFJ Securities
Bank of England