The MSc in Quantitative Finance is a balanced programme of finance and computational methods, which will suit numerate, competitive students looking for careers in finance that stretch their quantitative talents.
Ambitious students with an educational background in Economics, Finance or STEM (Science, Technology, Engineering, and Mathematics) subjects and with a proven quantitative talent will be ideally suited to this programme.
This course covers a broad range of subjects related to the mathematical modelling of financial markets and the pricing and hedging of financial securities. The course equips you with the necessary theoretical, mathematical and computational skills needed to pursue a career in quantitative finance. If you are a competitive student looking for a career in finance that will use your quantitative talents to the full, this is the course for you.
For graduates wishing to pursue further the theoretical dimension to the discipline, the curriculum content provides the perfect basis for a Ph.D. in Finance.
The course was awarded Risk Management Accreditation by the Professional Risk Managers International Association (PRMIA), confirming the suitability of the course for preparing graduates for a career as professional risk managers. Graduating students obtain exemptions to PRMIA level 1 and 2 exams.
The programme can be undertaken on a part-time basis, thus enabling current professionals with the relevant background to combine working life with study. However, if you are in full-time employment, employer support is essential to undertake the part-time option as it requires class attendance and some work on group assignments during the working day.
What will I learn?
The course achieves the ideal balance between financial and computational theory and methodology, thereby enabling graduates to critically analyse and develop financial decision-making and risk management tools but also understand specific quantitative models and strategies applied in financial markets.
1. Acquisition of the theoretical, analytical and practical skills needed to manage portfolios of equity, fixed income and derivative securities and develop the tools for managing corporate financial risk.
2. Assimilation of implementation methods for financial models using various programming languages and the application of critical evaluation techniques to the performance of models.
3. The ability to carry out independent research on the uses of financial models, their implementation and their limitations.
How will I benefit?
The course not only attracts graduates from a number of different backgrounds but can also be tailored to a wide variety of academic and professional objectives, providing an enviable array of career options upon graduation.
• A truly specialist preparation for a future career within the finance industry, in a variety of functions including funds management, investment banking, financial engineering and corporate treasury management.
•In the summer term, students can choose from some summer term modules, or a research project, or in a small number of cases, from a limited number of possible internships. With any possible internship opportunity, students may have to go through a competitive process for these, including potential interviews.
•Exemption from Professional Risk Managers certification examinations, the global standard for the world's top financial risk professionals.
Course features
Industry recognition and endorsement the course was awarded Risk Management Accreditation by the Professional Risk Managers International Association, confirming the suitability of the course for preparing graduates for a career as professional risk managers.
A customisable curriculum the course is available in three made-to-measure "streams": 12 taught modules, research + major project, or modules + internship.
Possibilities within academia for graduates wishing to pursue further the theoretical dimension to the discipline, the curriculum content provides the perfect basis for a Ph.D. in Finance.
Comment
Student profile
Ambitious students with an educational background in mathematics or economics and a proven quantitative talent will be ideally suited to this course, as well as statistics, physics, computer science or engineering students capable of going through mathematically rigorous finance training. If you are in full-time employment, employer support is essential to undertake the part-time option as it requires class attendance during the working day.