MSc in Financial and Computational Mathematics

Key Points

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Modern finance increasingly relies on advanced mathematical and computational techniques for the modeling of financial and asset market movements, the design and valuation of financial derivatives, and portfolio management. This course provides an appropriately rigorous treatment of the branches of mathematics applicable to financial modeling, including measure theory probability, stochastic processes in discrete and continuous time, and partial differential equations. It is mathematically challenging and requires prior familiarity with multivariate calculus, differential equations, linear algebra, probability, and statistics. You should also have some programming experience. The rapid increase in available computing speeds over the past fifteen years has led to the widespread adoption of sophisticated computational methods for financial modeling and the development of algorithmic approaches to market trading. Computational methods form a fundamental part of this course; we provide exposure to relevant software, including Python, R and C#, and provide the option to study machine learning, which is becoming an essential and rapidly developing tool in industry.

Career options

Employers in the banking and investment sector require graduates with a thorough understanding of the relevant mathematical concepts, as well as the practical and computational skills associated with their application. This course provides both, and is an opportunity for students pursuing mathematics degrees that are not specifically financial in nature to continue their studies in advanced mathematics with a financial focus and thereby enhance their employment options.

Program structure

In semesters 1 and 2 you can expect to attend an average of 12 hours of lectures and 6-8 hours of tutorials and lab sessions per week, which will be evenly distributed throughout the working day. The rest of your time will be spent on independent study, exercises and assignments.

Semester 3 you will be fully engaged in a substantial research project that requires you to write, present and defend a thesis.

PART I

Main module (45 credits)

Probability Theory in Finance (10 credits)
Derivatives, Securities and Option Pricing (5 credits)
Computational Finance I (5 credits)
Computational Finance II (5 credits)
Topics in Financial Mathematics (5 credits)
Continuous-time financial models (5 credits)
Numerical methods and applications (5 credits)
Optimization (5 credits)

Elective modules (choose 15 credits)

Applied Stochastic Differential Equations (5 credits)
Scientific computing with numerical examples (10 credits)
Partial Differential Equations (5 credits)
Data Analysis II (5 credits)
Implementations of Statistical Analysis I (5 credits)
Implementations of Statistical Analysis II (5 credits)
Introduction to relational databases (5 credits)

PART II

Thesis in Financial and Computational Mathematics (30 credits)

Admission requirements

Candidates must have obtained at least an honours degree or equivalent in mathematical sciences or other highly numerate discipline.
Candidates who have earned at least a Bachelor’s degree with honors in Engineering or Physics may be considered, but are expected to have sufficient experience in college-level mathematics as assessed by the Course Coordinator.
In the case of international candidates, the foreign equivalent is required. In addition, a degree officially translated into English will be required.
IELTS 6.5, with no less than 6.0 in any component (or internationally (or its internationally recognised equivalent).