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MSc Financial Modelling and Optimization

  • DeadlineStudy Details:

    1 year full time, 2 years part time

Course Description

This programme gives you a flexible syllabus to suit the demands of employers that use modern financial tools and optimization techniques in areas such as the financial sector and energy markets.

We will give you sound knowledge in financial derivative pricing, portfolio optimization and financial risk management.

We will also provide you with the skills to solve some of today’s financial problems, which have themselves been caused by modern financial instruments. This expertise includes modern probability theory, applied statistics, stochastic analysis and optimization.

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.

Fees

Please see our website

Student Destinations

Graduates have gone on to work in major financial institutions or to continue their studies by joining PhD programmes.

Module Details

Compulsory courses have previously included:

  • Discrete-Time Finance
  • Stochastic Analysis in Finance
  • Fundamentals of Optimization
  • Research Skills for Financial Mathematics
  • Risk-Neutral Asset Pricing
  • Numerical Probability and Monte Carlo
  • Optimization Methods in Finance

Optional courses have previously included:

  • Fundamentals of Operational Research
  • Stochastic Modelling
  • Finance, Risk and Uncertainty
  • Python Programming
  • Algorithmic Game Theory and its Applications*
  • Time Series
  • Financial Risk Theory
  • Large Scale Optimization for Data Science
  • Credit Scoring
  • Stochastic Control and Dynamic Asset Allocation
  • Machine Learning in Python
  • Numerical Partial Differential Equations

*delivered by the School of Informatics

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