• Birmingham City University Featured Masters Courses
  • Northumbria University Featured Masters Courses
  • University of Surrey Featured Masters Courses
  • Ulster University Featured Masters Courses
  • Xi’an Jiaotong-Liverpool University Featured Masters Courses
  • University of Bristol Featured Masters Courses
  • University of Derby Online Learning Featured Masters Courses
King’s College London Featured Masters Courses
University of Leicester Featured Masters Courses
University College London Featured Masters Courses
Cranfield University Featured Masters Courses
University of Dundee Featured Masters Courses

Course Content

Course content

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.

Adding depth to your learning, our work placement programme puts you at the heart of financial organisations such as Moody's Analytics, Standard Life Investment and Lloyds Banking Group.

Programme structure

This programme involves two taught semesters of compulsory and option courses, followed by a dissertation project. You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take a number of compulsory courses and optional courses. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project worth 60 credits, possibly with one of our industry partners, for the award of the MSc degree.

Compulsory courses:

  • Discrete-Time Finance (10 credits, S1)
  • Stochastic Analysis in Finance (20 credits, S1)
  • Fundamentals of Optimization (10 credits, S1)
  • Research-Linked Topics (10 credits, full-year)
  • Finance, Risk and Uncertainty (10 credits, S1)
  • Risk-Neutral Asset Pricing (10 credits, S2)
  • Simulation (10 points, S2)
  • Optimization Methods in Finance (10 credits, S2)

Optional courses:

  1. Operations Research and Mathematical Finance courses:
  • Financial Risk Theory (10 credits, S1)
  • Computing for Operational Research and Finance (10 credits, S1)
  • Fundamentals of Operational Research (10 credits, S1)
  • Stochastic Control and Dynamic Asset Allocation (10 credits, S2)
  • Credit Scoring (10 credits, S2)
  • Financial Risk Management (10 credits, S2)
  • Risk Analysis (5 credits, S2)
  • Stochastic Modelling (10 credits, S2)
  1. Relevant Statistical and Numerical courses:
  • Multivariate Data Analysis (10 credits, S2)
  • Numerical Partial Differential Equations (10 credits, S2)
  • Advanced Time Series Econometrics (10 credits, S2) (offered by the School of Economics)
  1. Programming courses:
  • Object-Oriented programming with applications (10 credits, S1)
  • Parallel Numerical Algorithms (10 credits, S1), (offered by EPCC)
  • Programming Skills (10 credits, S1), (offered by EPCC)
  1. Optimization courses:
  • Combinatorial Optimization (5 credits, S2)
  • Large Scale Optimization for Data Science (10 credits, S2)
  • Modern Optimization Methods for Big Data Problems (10 credits, S2)
  • Nonlinear Optimization (10 credits, S2)
  • Stochastic Optimization (5 credits, S2)

Work placements/internships

We work closely with the Scottish Financial Risk Academy (SFRA) to offer a number of short courses led by industry (part of our Research-Linked Topics) and to provide the opportunity to our best students to write their dissertations during placements with financial services companies.

Learning outcomes

At the end of this programme you will have:

  • developed personal communications skills, initiative, and professionalism within a mathematical context
  • developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
  • improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
  • mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
  • developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

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

Visit the Financial Modelling and Optimization (MSc/PgDip) page on the University of Edinburgh website for more details!




Enquire About This Course

Recipient: University of Edinburgh

* required field

Please correct the errors indicated below to send your enquiry

Your enquiry has been emailed successfully

Cookie Policy    X