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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.
This course is also available on a part-time basis.
Why we're great.
Study in our Centre for Computational Finance, internationally renowned for leading edge, interdisciplinary work
Work alongside experts who’ve applied their findings to advise industry and the UK government
Our Employability and Careers Centre is on hand to help with careers advice and planning. You will also have opportunities to present your research and travel to international conferences
Our expert staff
This course is taught by experts with both academic and industrial expertise in the financial and IT sectors. We bring together leading academics in the field from our departments of economics, computer science and business.
Our staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management.
Specialist facilities
We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
All computers run either Windows 7 or are dual boot with Linux
Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors
Your future
We have an extensive network of industrial contacts through our City Associates Board and our alumni, while our expert seminar series gives you the opportunity to work with leading figures from industry.
Our recent graduates have gone on to become risk analysts and managers, asset managers, managing directors and CEOs at a range of global, national and regional organisations, including HSBC, London Stock Exchange and Deutsche Bank.
We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.
Entry requirements
UK entry requirements
2.2 degree in Finance, Financial Economics, Economics, Engineering, Mathematics, Statistics, Physics or Computer Science.
We will accept graduates of any other degree but this must contain Mathematics (calculus) or Econometrics (probability, Statistics) Also some programming experience is required.
International & EU entry requirements
We accept a wide range of qualifications from applicants studying in the EU and other countries. Get in touch with any questions you may have about the qualifications we accept. Remember to tell us about the qualifications you have already completed or are currently taking.
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English language requirements
IELTS 6.0 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
Additional Notes
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Structure
Example structure
Most of our courses combine compulsory and optional modules, giving you freedom to pursue your own interests. All of the modules listed below provide an example of what is on offer from the current academic year. Our Programme Specification provides further details of the course structure for the current academic year.
Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.
The example structure below is representative of this course if taken full-time. If you choose to study part-time, the modules will be split across 2 years.
The module introduces students to financial markets as well as providing a detailed introduction to the quantitative methods that are a pre-requisite to other CCFEA modules. Students will be introduced to financial markets such as equities, bonds, interest rates, forwards, futures and foreign exchange. Applications of calculus and statistical methods to finance are also presented.
This module focuses on quantitative methods in finance and economics and their application to investment, risk management and trading. The module will introduce students to state-of-the-art statistical modelling of financial markets and will give an overview of the quantitative framework that is necessary to advance to other CCFEA modules.
The modules introduces students to computational thinking in economics and finance by looking at different relevant models and theories, such as agent-based modelling and game theory. Students will also be introduced to various applications, such as financial forecasting, automated bargaining and mechanism design.
This module aims to prepare students for conducting an independent research project leading to a dissertation and to provide them with an appreciation of research and business skills related to their professional career. As a precursor to their project students, individually select an area of Computer Science, or Electronic Engineering, or Computational Finance and perform the necessary background research to define a topic and prepare a project proposal under the guidance of a supervisor. The module guides them by a) introducing common research methods b) creating an understanding of basic statistics for describing and making conclusions from data c) helping to write a strong proposal including learning how to perform literature search and evaluation and d) giving an in-depth view into the business enterprise, financial and management accounting and investment appraisal.
This module is a mix of theory and practice with big data cases in finance. Algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structures and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.
Acquire critical and transferable skills associated with the creation and growth of new business ventures. You focus on the development process from start up to early stage growth of new ventures, new small businesses spin offs from large firms, and especially innovative, technology-based firms. You study opportunity identification, self-efficacy, ideas generation, bricolage and bootstrapping, developing business models, networking, marketing, and finance.
The aim of this module is to provide an introduction to computer programming for students with little or no previous experience. The Python language is used in the Linux environment, and students are given a comprehensive introduction to both during the module. The emphasis is on developing the practical skills necessary to write effective programs, with examples taken principally from the realm of data processing and analysis. You will learn how to manipulate and analyse data, graph them and fit models to them. Teaching takes place in workshop-style sessions in a software laboratory, so you can try things out as soon as you learn about them.
Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others.
Mathematics is a tool used in many fields of research, and this module introduces students to techniques and ways of thinking designed to enable them to carry out their own mathematical investigations, or to apply mathematical ideas to an investigation of their own (typically for most students on this module, this will be their Dissertation project). We use the industry standard mathematical software Matlab, although the techniques introduced can also be applied using other software, and we study a range of techniques for numerical computation and processing of data.
The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) in computer science and artificial intelligence. This will allow the student to build computers and intelligent machines which are able to have an artificial brain which will allow them to learn and adapt in a human like fashion.
Computational Market Microstructure for FinTech and the Digital Economy
Equip yourself with principles of allocation and mechanism design from an operational perspective. Auction design and market microstructure of the stock market, liquidity provision in electronic financial markets such as dark pools, and capital adequacy of centralized clearing platforms are some of the specific applications that will be studied in the first part of this module. During the second part, you will be introduced to complexity economics of self-organisation, network modules, and strategic proteanism. Finally, you'll use network models to study economic interactions.
Taught modules for the first two terms, followed by a dissertation in the summer
Study is highly practical and involves both lectures and hands-on laboratory sessions
Analyse and model real world financial data
Attend lectures given by practitioners, including senior staff from HSBC, Olsen Ltd, Royal Bank of Scotland and the Financial Services Authority
Assessment
Courses are awarded on the results of your written examinations, together with continual assessments of your practical work and coursework
Dissertation
Many dissertations have formed the basis of published research papers
Students have been invited to present at international conferences and renowned institutions, such as the Bank of England
Fees and funding
Home/EU fee
£9,500
International fee
£18,460
Fees will increase for each academic year of study.
What's next
Open Days
We hold Open Days for all our applicants throughout the year. Our Colchester Campus events are a great way to find out more about studying at Essex, and give you the chance to:
tour our campus and accommodation
find out answers to your questions about our courses, student finance, graduate employability, student support and more
meet our students and staff
If the dates of our organised events aren’t suitable for you, feel free to get in touch by emailing tours@essex.ac.uk and we’ll arrange an individual campus tour for you.
We aim to respond to applications within two weeks. If we are able to offer you a place, you will be contacted via email.
For information on our deadline to apply for this course, please see our ‘how to apply’ information.
Visit Colchester Campus
Home to 15,000 students from more than 130 countries, our Colchester Campus is the largest of our three sites, making us one of the most internationally diverse campuses on the planet - we like to think of ourselves as the world in one place.
The Campus is set within 200 acres of beautiful parkland, located two miles from the historic town centre of Colchester – England's oldest recorded town. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour.
If you live too far away to come to Essex (or have a busy lifestyle), no problem. Our 360 degree virtual tour allows you to explore the Colchester Campus from the comfort of your home. Check out our accommodation options, facilities and social spaces.
Exhibitions
Our staff travel the world to speak to people about the courses on offer at Essex. Take a look at our list of exhibition dates to see if we’ll be near you in the future.
At Essex we pride ourselves on being a welcoming and inclusive student community. We offer a wide range of support to individuals and groups of student members who may have specific requirements, interests or responsibilities.
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