Master of Science by Coursework in Mathematical and Computational Finance

  • 1. The Divisional Board of Mathematical, Physical and Life Sciences shall appoint for the supervision of the course a supervisory committee, which shall have the power to approve lectures and other instruction. The committee shall appoint a course organiser who will be responsible for ensuring that the programme is set up and the decisions of the committee are carried out.

  • 2. The course organiser shall arrange for the appointment of a supervisor for each candidate.

  • 3. Each candidate shall follow a course of study in Mathematical and Computational Finance for at least three terms and for a substantial part of the intervening vacations.

  • 4. The examination will consist of the following parts:

    • (i) Two written examinations, and one take-home project, which will cover the Michaelmas Term core courses in mathematical methods and numerical analysis, based on the schedule below. The written examinations will be organised within the department.

    • (ii) Candidates will be assessed on either the ‘Modelling’ Stream (covering Hilary Term modelling courses) or the ‘Data Driven’ Stream (covering Hilary Term data driven courses). The ‘Modelling’ Stream will be assessed by a written examination. The ‘Data Driven’ Stream will be assessed by a written examination and a computer based practical examination. Further details will be specified in the Course Handbook on the Course Website. Examinations will be organised within the Department.

    • (iii) Candidates will be assessed on a ‘Tools’ Stream (covering Hilary Term courses on tools). The ‘Tools’ Stream will be assessed by a written examination. Further details will be specified in the Course Handbook on the Course Website. The examination will be organised within the Department.

    • (iv) One course in Quantitative Risk Management which will assessed by a take-home project.

    • (v) Two courses in Financial Computing with C++ which will be assessed by two practical [For students starting before MT 2018: examinations arranged ][For students starting from MT 2018: assessments ] within the Department. The details will be specified in the Course Handbook on the Course Website.

    • (vi) A dissertation of between twenty-five and forty pages on a topic approved by the examiners.

  • More detail on these requirements will be set out each year in the Course Handbook on the Course Website.

  • 5. Take-home projects shall be submitted electronically. Submission shall be in accordance with both the details given in the Course Handbook on the Course Website and with the deadlines which the examiners shall determine and notify candidates of. In exceptional cases where a candidate is unable to submit work electronically, he or she must apply to the Standing Committee for permission to submit the work in paper form to the Examiners, c/o the Academic Administrator for Mathematical Finance, Mathematical Institute. Such applications must reach the Mathematical Institute not less than two weeks before the deadline for submitting the work.

  • 6. Three copies of the dissertation must be delivered not later than noon on a date to be specified by the examiners which will normally be in late June, to the Examiners, M.Sc. in Mathematical and Computational Finance, c/o Examination Schools, High Street, Oxford OX1 4BG. A copy of the dissertation in pdf or other machine-readable format shall also be made available, in accordance with instructions which the examiners shall determine and notify candidates of. Candidates will also be required to give an oral presentation based on their dissertation.

  • 7. The examiners may award a distinction for excellence in the whole course.

  • 8. A candidate who fails the examination will be permitted to retake it on one further occasion only, not later than one year after the initial attempt. In such a case the examiners will specify at the time of failure which components of the examination may or must be redone.

Schedule

Mathematical methods including stochastic analysis, partial differential equations, probability and statistics.

Mathematical models of financial markets; associated topics in financial economics.

The numerical solution of ordinary, partial and stochastic differential equations.

Monte Carlo methods.

Numerical methods for optimisation.

Programming in appropriate languages, and use of relevant packages.