MSc Financial and Computational Mathematics
Modern finance is increasingly reliant upon advanced mathematical and computational techniques for the modelling of asset and financial market movements, the design and valuation of financial derivatives, and portfolio management.
This course provides an appropriately rigorous treatment of branches of mathematics applicable to financial modelling, including measure-theoretic 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 experience of programming.
The rapid increase in available computing speeds over the past fifteen years has led to the widespread adoption of sophisticated computational methods for financial modelling and the development of algorithmic approaches to market trading.
Computational methods form a core 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 emerging as an essential and rapidly developing tool in industry.
PART 1
Core Module (45 credits)
Elective Modules (Choose 15 credits)
PART 2
Note: Module selection must be approved by the module co-ordinator.
Modern finance is increasingly reliant upon advanced mathematical and computational techniques for the modelling of asset and financial market movements, the design and valuation of financial derivatives, and portfolio management.
This course provides an appropriately rigorous treatment of branches of mathematics applicable to financial modelling, including measure-theoretic 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 experience of programming.
The rapid increase in available computing speeds over the past fifteen years has led to the widespread adoption of sophisticated computational methods for financial modelling and the development of algorithmic approaches to market trading.
Computational methods form a core 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 emerging as an essential and rapidly developing tool in industry.
PART 1
Core Module (45 credits)
Elective Modules (Choose 15 credits)
PART 2
Note: Module selection must be approved by the module co-ordinator.
