4. The MSc Course

4.1 Aims of the Programme

The programme aims:
1.  to provide you with a strong mathematical background with the necessary to apply your expertise to the solution of real finance problems.
2. to provide you with a systematic understanding of core areas in mathematical models, techniques, numerical methods and data analysis in finance as well as source advanced topics in one or more of these areas.
3. to develop your skills so that you are able to formulate a well posed problem from a description in financial language, carry out relevant mathematical and/or statistical analysis, develop an appropriate numerical scheme and/or statistical algorithm, present and interpret these results.
4. to lay the foundation for further research or for a career as a quantitative analyst into a financial or other institution.
More information about the programme can be found on the Course Website.

4.2 Programme Outcomes

A.

Students will gain a knowledge of: Related teaching/learning methods and assessment
1. Core Courses Lectures and classes in terms 1 & 2, written examinations in January and April.
Two take home projects
2. Courses Lectures and Classes in term 2. Students to choose 4 elective courses out of 6. Assessed by written examination in April.
3. Practical Computational Finance An intensive 8-hour non-examinable lecture course on Python will also be run in Introductory week.
Two C++ lecture courses are supported by practicals which are built around computational finance, and these courses are assessed by practical online examinations taking place in January and March.
4. Dissertation on a specific problem

Students write a report of between 25 and 40 pages in some depth on a specific problem.

B. Students have the opportunity to develop the following skills during the course

I. Intellectual skills
The ability to demonstrate knowledge of key mathematical and financial concepts and topics, both explicitly and by applying them to the solution of problems.
The ability to comprehend problems, abstract the essentials of problems and formulate them mathematically and in symbolic form so as to facilitate their analysis and solution.
The ability to grasp how mathematical processes may be applied to problems, including where appropriate, an understanding that this might give only a partial solution.
The ability to select and apply appropriate mathematical processes.
The ability to construct and develop logical mathematical arguments with clear identification of assumptions and conclusions.
The ability to use computational and more general IT facilities as an aid to mathematical processes and for acquiring any further information that is needed and available.
The ability to present mathematical arguments and conclusions from them with clarity and accuracy, in forms suitable for the audiences being addressed
The ability to formulate a financial problem in mathematical terms, solve the resulting equations analytically or numerically, and give interpretations of the solutions

Teaching/learning methods and strategies

These are acquired through lectures, classes, practical classes, studying recommended textbooks and through work done for mini-projects and dissertations.

II. Mathematical related practical skills Teaching/Learning methods and strategies
1. Calculating fluently and accurately in abstract notation. Practiced throughout the course in problem work for classes.
2. Use of mathematics computer packages
especially C++ and Python.		 Practiced throughout course especially in practical numerical analysis classes.

III. General skills Teaching/Learning methods and strategies
1. To analyse and solve problems, and to reason logically and creatively. Mathematical problem sheets with class support often requiring significant development of ideas beyond material found in lectures and books.
2. Effective communication and presentation orally. Presentation of solutions in classes.
3. The ability to learn independently. The dissertation and mini-projects require students to put together material from a number of sources including lectures, textbooks, and electronic sources, in their own time.
4. Independent time management. Requirement to produce substantial amounts of written work against class deadlines; necessity to balance academic and non-academic activities without continuous oversight.
5. To think critically about solutions and to defend an intellectual position. Discussion and criticism in classes and
with supervisor.
6. Collaboration. Modelling classes involve group work so that students share ideas and develop the practice of crediting others for their contributions.
7. Use of information and technology. Compulsory practical work; extensive use of computing techniques and data analysis.

4.3 Course Structure

Below is a short outline of the course structure. For the list of courses go to Section 5.

4.3.1 Introductory Courses

You take five introductory courses in the induction (introductory) week (Week -1 of Michaelmas Term). These are the foundation courses necessary for the rest of the course.

4.3.2 Core Courses: Michaelmas Term

The first term focuses on core material that is compulsory for all students; the term offers 64 hours or lectures and 24 hours of classes/practicals.

4.3.3 Core and Elective Courses: Hilary Term

The second term focuses on core material that is compulsory for all students as well as elective courses. The term offers 40 hours of lectures and 32 hours of classes/practicals for the core courses. Students are to choose 4 out of 6 elective courses. Each elective course comprises 8 hours of lectures and 2 classes.

4.3.4 Financial Computing Courses

The first component of the Financial Computing course, Financial Computing with C++1 (16 hours of lectures and 4 two-hour classes) is held in Michaelmas Term.
The second component of the Financial Computing course, Financial Computing with C++2 (24 hours of lectures and practicals in total) is held in Hilary Term.

4.3.5 Dissertation

The third term is dedicated to a dissertation project which is to be written on a topic chosen in consultation with a supervisor. There will be the option of an internship alongside your dissertation. We will circulate further information in due course.

4.3.6 Internships and visas

You may undertake an internship alongside your dissertation in Trinity term for the MSc in Mathematical and Computational Finance (MCF).

The student visa work permission prohibits students working for more than 20 hours a week during term time, but you are allowed to work without an hours restriction during vacation periods and after the end of the course up until your student visa expiry date. For the MCF, vacation is defined as Monday of 10th week of Michaelmas term to the day before the start of the 0th week of Hilary term (i.e. Saturday), and Thursday of 10th week of Hilary term to the day before the start of the week preceding 0th week of Trinity term (i.e. the Saturday).

For the purposes of being allowed to work without an hours restriction, the end of the course is defined as the cohort submission deadline for the final piece of work for the course. Students should not submit their dissertation early in order to undertake work without an hours restriction.

If you choose to do an internship during a period of vacation, or after the end of your course as defined above, no special permission is required, as you are already allowed to work without an hours restriction. If you want to do a full-time internship during Trinity term and/or before the course end date, however short, it must meet the Home Office criteria for a 'work placement' to be permitted on your student visa. A 'work placement' must be an 'assessed and integral' part of your course. This may include where the work placement is the subject of your dissertation. You need to agree this with the Mathematical Institute before you start the internship so that we can confirm it meets these requirements.

Students are not permitted to undertake an internship during Michaelmas or Hilary term.