Postgraduate Course
MSc Computational Finance
Overview
The details
Computational Finance
October 2018
Full-time
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 quantitative analysts, portfolio managers and software engineers at various institutions, including:
- HSBC
- Mitsubishi UFJ Securities
- Old Mutual
- Bank of England
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.
Sorry, the entry requirements for the country that you have selected are not available here.Please select your country page where you'll find this information.
English language requirements
IELTS 6.0 overall with a minimum component score of 5.5If 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.
CCFEA MSc Dissertation
This dissertation is worth% and is submitted to FASer and the school in week 48. The presentation is worth 10% and takes place in weeks 49/50.
Financial Engineering and Risk Management
Introduction to Financial Market Analysis
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.
View Introduction to Financial Market Analysis on our Module Directory
Computational Models in Economics and Finance
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.
View Computational Models in Economics and Finance on our Module Directory
Professional Practice and Research Methodology
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.
View Professional Practice and Research Methodology on our Module Directory
Quantitative Methods in Finance and Trading
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.
View Quantitative Methods in Finance and Trading on our Module Directory
Industry Expert Lectures in Finance
Mathematical Research Techniques Using Matlab (optional)
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.
View Mathematical Research Techniques Using Matlab (optional) on our Module Directory
Introduction to Programming in Python (optional)
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.
View Introduction to Programming in Python (optional) on our Module Directory
Neural Networks and Deep Learning (optional)
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.
View Neural Networks and Deep Learning (optional) on our Module Directory
Machine Learning and Data Mining (optional)
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.
View Machine Learning and Data Mining (optional) on our Module Directory
Creating and Growing a New Business Venture (optional)
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.
View Creating and Growing a New Business Venture (optional) on our Module Directory
Data Science and Decision Making (optional)
The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decision using data. This module blends data analysis, decision making and visualisation with practical python programming.Students will need a reasonable programming background as they will be expected to develop a complete end-to-end data science application.
View Data Science and Decision Making (optional) on our Module Directory
Computational Market Micro-Structure and Complexity Economics (optional)
Equip yourself with the 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.
Teaching
- Taught over one year on a full-time basis
- 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
£7,560
International fee
£16,225
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.
2018 Open Days (Colchester Campus)
- Saturday, September 15, 2018
- Saturday, October 27, 2018
Applying
You can apply for our postgraduate courses online. You’ll need to provide us with your academic qualifications, as well as supporting documents such as transcripts, English language qualifications and certificates. You can find a list of necessary documents online, but please note we won’t be able to process your application until we have everything we need.
There is no application deadline but we recommend that you apply before 1 July for our taught courses starting in October. We aim to respond to applications within two weeks. If we are able to offer you a place, you will be contacted via email.
Visit Colchester Campus
We want you to throw yourself in at the deep end, soak up life and make the most of those special Essex moments.
Home to over 13,000 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.
Virtual tours
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.
The University makes every effort to ensure that this information on its course finder is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements, industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to courses may for example consist of variations to the content and method of delivery of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such changes to a minimum, and will also keep prospective students informed appropriately by updating our programme specifications.
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