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MSc Computational Mathematical Finance

  • DeadlineStudy Details:

    1 year full time, 2 years part time

Course Description

The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.

Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.

This MSc programme delivers:

  • a flexible programme of study relevant to the needs of employers such as: top investment banks, hedge funds and asset management firms
  • a solid knowledge in financial derivative pricing, risk management and portfolio management
  • the transferable computational skills required by the modern quantitative finance world

The School of Mathematics has connections with a wide number of industrial partners, organisations, charities and government departments. As an MSc student in the School you will have opportunities to engage with these external organisations through regular employability events, careers workshops, and our annual analytics challenge (whose recent partners have included Edinburgh Airport and the Data and Marketing Association). There is also the opportunity to undertake a dissertation project with an external partner.

Entry Requirements

A UK 2:1 degree, or its international equivalent, in mathematics or a mathematical subject such as statistics, physics or engineering. You must also have relevant programming experience (at least one semester undergraduate programming course, in any language e.g. C, C++, Java, Python, passed at 2:1 level).

Fees

Please see our website

Student Destinations

Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.

Module Details

This programme involves two taught semesters of compulsory and optional courses followed by your dissertation project. There are three streams: the Financial stream, the Computational stream and the Machine Learning stream. Each stream features different sets of compulsory and optional courses.

There are three streams: the Financial stream, the Computational stream and the Machine Learning stream*. Each stream features different sets of compulsory and optional courses.

Compulsory courses (for all streams) have previously included:

  • Stochastic Analysis in Finance
  • Discrete-Time Finance
  • Python Programming
  • Numerical Probability and Monte Carlo
  • Risk-Neutral Asset Pricing
  • Stochastic Control and Dynamic Asset allocation
  • Research Skills for Financial Mathematics

Financial stream compulsory courses have previously included:

  • Financial Risk Theory
  • Optimization Methods in Finance

Computational stream compulsory courses have previously included:

  • Time Series
  • Numerical Partial Differential Equations

Machine Learning stream compulsory courses have previously included:

  • Machine Learning in Python

Optional courses have previously included:

  • Blockchains and Distributed Ledgers*
  • Programming Skills*
  • Finance, Risk and Uncertainty
  • Bayesian Theory
  • Reinforcement Learning*
  • Algorithmic Game Theory and its Applications*
  • Financial Risk Theory
  • Credit Scoring
  • Optimization Methods in Finance
  • Bayesian Data Analysis
  • Integer and Combinatorial Optimization
  • Time Series
  • Numerical Partial Differential Equations

*delivered by the School of Informatics

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