Programme Specification: MSc/PG Dip in Financial Mathematics and Computation

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1 Programme Specification: MSc/PG Dip in Financial Mathematics and Computation Course Outline Financial Mathematics is an application of mathematical methods to financial markets and management, using advanced computer technology to predict the behaviour of the markets and suggest strategies for investment. The M.Sc. in Financial Mathematics and Computation is designed to provide a mature understanding of financial mathematics and appropriate computational techniques. The focus of the course is on computational techniques for finance, on mathematical modelling and on mathematical and economical theories of finance. The M.Sc. in Financial Mathematics and Computation is a prestigious qualification. The quantitative finance area is widely recognised as a rapidly expanding market for mathematical and scientific skills. The main areas in which Financial Mathematics is applied are modelling and forecasting financial markets, derivative instruments and securities, hedging and financial risk management, asset allocation and investment management, quantitative trading and arbitrage, asset/liability management, quantitative issues in corporate and public financial policy, computer and information technology in the financial services industry. Pathways Except for the choice of optional modules, the taught part of the programme is identical for all students. Students who satisfactorily complete it will be awarded a Diploma. The choice of optional modules for each student will be considered on an individual basis taking into account student s background in Mathematics and Economics, student s aims and preferences. Those who wish to be awarded the MSc and, after the examinations, qualify for doing so, can undertake a full-time individual project, leading to the submission of a dissertation by the middle of September. Progression rules are detailed in full below. 1

2 Programme Specification MSc/PG Dip in Financial Mathematics and Computation Entry requirements: The entry requirements are at least a good second class honours BSc degree or qualification of equivalent standard recognised by the University in physics, engineering or mathematics. In general, it is expected that a student has a solid background in mathematics (calculus, linear algebra, ordinary differential equations, basics of probability and statistics). However, a pre-session two-week intensive course can be arranged in order to improve or refresh the knowledge of basic mathematical tools. Because applications are treated on an individual basis, alternative qualifications, including work experience, may be considered. A candidate for admission whose first language is not English must have a qualification in the English language before he/she can be admitted to the programme. A student whose first degree studies were conducted at a university in an English-speaking country need not possess one of these qualifications, provided an academic referee is able to confirm proficiency in English. Aims and Objectives Students on this course are expected to acquire knowledge and understanding of Financial Mathematics and computational techniques for finance that will equip them to enter competitively the pool of potential employees of investment banks and other financial institutions. By the end of the course, students should be able to formulate problems from finance in mathematical terms, select and develop an appropriate numerical method, write a computer program to numerically approximate the problem, and present and interpret these results for a potential client. A wide range of career opportunities is available to graduates in Financial Mathematics: commercial and investment banks, brokerage and investment firms, insurance companies, consulting and accounting firms, treasury departments of nonfinancial corporations, public institutions, such as state and local governments and international organizations, software and technology vendors providing products and services to the financial industry. 2

3 Course Content and Structure The course consists of 120 credits of taught material (60 credits per semester) divided into core and optional modules and a 60 credit dissertation during summer. First semester Taught Modules (each carrying a 15 credit rating) Core Options (three from the list) Mathematics Financial mathematics I Dynamical Modelling and Simulation Wavelets Generalised linear models Economics Principles of finance Financial systems and institutions Computer Science C++ programming Second semester Core Options (three from the list, at least one from Maths) Mathematics Financial mathematics II Stochastic numerics (not run in 2005/6) Computational methods for partial differential equations Operational research Data Mining and Neural Networks Economics International money and finance Financial econometrics Corporate Finance Computer Web Technologies Science Management Risk Management Centre Optional courses are subject to availability in each particular year and will be revised annually in consultation with the departments involved. The teaching of these modules is shared with the corresponding Level 3, Level 4, Level 7 modules of degrees in Mathematics, Economics, CSc, Engineering and Management. INDIVIDUAL PROJECT (60 credits). After examinations, a project is undertaken full-time, leading to submission of a dissertation by the middle of September. Typical length of the dissertation is about words, but no precise minimum length is prescribed, as this will depend on the particular topic chose, the amount of software development involved, and the applications component. The project is expected to contain some element of original work. Students will typically complement the foundational material of the first two semesters with practical, applied work during the project. 3

4 Subject and Professional Skills Intended Outcomes Teaching Methods How demonstrated Knowledge Advanced knowledge of a range of mathematical topics in financial mathematics and scientific computing. Integration of knowledge across subjects. Concepts Computational and mathematical modelling, mathematical abstraction, generalisation, justification, and precision. Techniques Programming of mathematical algorithms, mastery of research methods, project planning. Critical Analysis Ability to apply understanding of concepts and techniques with independence, rigour & selfreflexivity. Presentation Ability to organise research material and or technology demonstration in a manner appropriate to the medium that is to be assessed; to distinguish between relevant and non-relevant material; to write-up and deliver oral reports on findings to a professional standard; to engage in scientific discussion with peers. Appraisal of evidence Ability to apply a numerical method for the solution of some real world problem. Ability to assess the efficacy of method used, both qualitatively and quantitively. Ability to assess the quality of a presentation, both oral and written. Independent research, and lectures. Lectures, computer practicals, coursework assignments. Lectures, computer labs. Independent research, lectures, coursework in modules. Supervision for project Lectures, project supervision. Examinations, coursework, oral presentations, computer demos, Examinations, coursework, oral presentations, computer demos, Oral presentations, computer demos, Oral presentations, participation in group discussions, essays/demos, Oral presentations, computer demos, Oral presentations, project plan, and 4

5 Transferable Skills Intended Outcomes Teaching Methods How demonstrated Managing Learning Identifying a credible research project, drawing up a realistic research time-table, reflecting on and writing up results Research Skills Progressive improvement in the ability to locate, organise and marshal evidence, report on findings, analyse complex ideas and construct sophisticated critical arguments. Working Relationships Knowing how and when to draw on the knowledge & expertise of others. Data Presentation Ability to present research clearly and effectively using appropriate IT resources. Communication Skills Ability to deliver oral presentations to professional standard; ability to respond to questioning; ability to write cogently and clearly. Programming Skills Ability to programme in a high level language. Coursework in modules. Through progressive modes of assessment, to the project plan, culminating in the Project supervision, lectures. Presentations during taught modules. Presentations during taught modules. Lectures. Various Computing modules, computing assignments in other taught modules. Oral presentations, completion of coursework, project plan, and Oral presentations, demos, project plan, and Dissertation. Oral presentations, demos, and Oral presentations, demos, project plan, and Computer practicals. 5