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Computational Finance | www.essex.ac.uk/ccfea Taught course modules MSc Computational Finance Derivative Securities Mathematical Research Techniques using MATLAB Introduction to Financial Market Analysis or Quantitative Finance and Market Analysis Learning and Computational Intelligence in Economics and Finance Financial Engineering and Risk Management Industry Expert Lectures in Finance MSc Financial Software Engineering Distributed Computing or Software Design and Architecture Advanced Relational and Object-Oriented Databases Underlying Technology in Financial Systems Introduction to Financial Market Analysis Financial Engineering and Risk Management Industry Expert Lectures in Finance MSc High Frequency Finance and Trading Finance and Empirical Market Microstructure Mathematical Research Techniques using MATLAB Introduction to Financial Market Analysis or Quantitative Methods in Finance and Trading Learning and Computational Intelligence in Economics and Finance Introduction to Java with Application to Simulation Modelling in Finance and Economics or Underlying Technology in Financial Systems High Frequency This information is a guide to course content and is subject to change on an annual basis. Academic staff Nick Constantinou, BSc Reading, MSc London, PhD Essex (Lecturer) (Essex Business School) Maria Fasli, BSc TEI Thessaloniki, PhD Essex (Senior Lecturer and Head of School) (School of Computer Science and Electronic Engineering) Wing Lon Ng, Dipl Kfm Cologne, Dr.res.pol Munster (Lecturer) (CCFEA) Sheri Markose, BA Bombay, MA Delhi, PhD London (Professor) (Department of Economics) Richard Olsen, Licentiate Zurich, MPhil Oxford, PhD Zurich (Visiting Professor) (CCFEA) Steve Phelps, BSc Sussex, PhD Liverpool (Lecturer) (CCFEA) Edward Tsang, BBA Hong Kong, MSc PhD Essex (Professor) (School of Computer Science and Electronic Engineering) Qingfu Zhang, BSc Shanxi, MSc Xidian (Professor) (School of Computer Science and Electronic Engineering and Department of Mathematical Sciences) 78 | Postgraduate Prospectus 2012
www.essex.ac.uk/ccfea | Computational Finance finance. We normally require our applicants to have a Masters degree at distinction level in a quantitative discipline, such as engineering, physics, computer science, statistics, mathematics or quantitative economics/finance. Our PhD students are normally jointly supervised by academic staff in two of our constituent departments, in order to give you an interdisciplinary perspective to your topic of research. Research areas Research supervision is available in the following fields: n Agent-based modelling of financial markets n Computational risk management n High frequency finance n Use of computational intelligence methods for investment decision making Agent-based models of financial markets view markets as self-organising systems. By simulating each participant in the market as an adaptive agent making use of inductive rules of thumb, we hope to build more realistic models than traditional neo-classical models, which assume all participants in the market are rational expected-utility optimisers. Agent-based models have the potential to provide policy makers and financial institutions with a powerful interactive tool to find answers for ‘what if’ questions and to perform ‘wind tunnel tests’ for market and policy design. Computational risk management applies computational methods to option pricing, interest rate models and credit-risk models. Our current topics include: group invariant solutions to the CIR model, pricing options using FFTs under a mean reverting process with stochastic volatility and jumps, ZCB prices in the Vasicek and CIR models, and solving the Asian option PDE using lie-symmetry methods. Lenka Hladova, Slovakia – BSc Economics ’09, Graduate Diploma Economics ’10, MSc Computational Finance Student profile Our research in high frequency finance includes developing real-time trading platforms, as well as deriving new financial econometric models for real-time data with a special focus on the asymmetric behaviour between the supply and demand sides of the market and the time varying information set that traders refer to before submitting their orders. Comprehending the market conditions under which traders either demand or supply liquidity leads to a better understanding of the price formation process. I decided to study at Essex because it was a big campus-based university, and offered many courses to choose from. I have always loved finance, especially the mathematical and statistical side of it. MSc Computational Finance offered me a great selection of modules which all looked very interesting and practical. The teaching in CCFEA is at a very high level, and all my lectures and laboratories are brilliant. I really appreciate that all my lecturers always try to answer our questions, and to make sure that we understand. It is also good that they all know your name and talk with you outside of your classes, so have given me some valuable advice about my future and further career. I think it is very important to have a good education, which Essex offers. During my studies, especially my Masters within CCFEA, I have realised what I would love to do in my career. Having a postgraduate qualification gives me an edge in the competitive world of work. My fondest memory of the University will be all the new people I have met; I have made very good friends with people from all over the world. Our research on computational intelligence for investment decision making includes reinforcement-learning for portfolio optimisation, the use of grammatical evolution and genetic programming for building algorithmic-traders which are able to detect and exploit patterns in volatility or other market indicators, the use of heuristic-optimisation algorithms for portfolio-optimisation in non-Gaussian environments with real-world constraints, and the use of genetic algorithms for estimation problems with ill-behaved likelihood functions. Postgraduate Prospectus 2012 | 79
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