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Computational Mathematical Finance (MSc)

Course Description

Programme 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

Programme structure

You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. 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, for the award of the MSc degree.

There are two streams: the Financial stream and the Computational stream.

Compulsory courses (both streams):

Stochastic Analysis in Finance (20 credits, semester 1)
Discrete-Time Finance (10 credits, semester 1)
Finance, Risk and Uncertainty (10 credits, semester 1)
Object-Oriented Programming with Applications (10 credits, semester 1)
Risk-Neutral Asset Pricing (10 credits, semester 2)
Stochastic Control and Dynamic Asset allocation (10 credits, semester 2)
Monte Carlo Methods (5 credits, semester 2)
Research-Linked Topics (10 credits, semesters 1 and 2)

Optional courses - Computational stream:

Numerical Methods for Stochastic Differential Equations [compulsory] (5 credits, semester 2)
Numerical Partial Differential Equations [compulsory] (10 credits, semester 2)
Programming Skills - HPC MSc (10 credits, semester 1)
Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)

Optional courses - Financial stream:

Financial Risk Theory [compulsory] (10 credits, semester 2)
Optimization Methods in Finance [compulsory] (10 credits, semester 2)
Advanced Time Series Econometrics (10 credits, semester 2)
Credit Scoring (10 credits, semester 2)
Computing for Operational Research and Finance (10 credits, semester 1)
Financial Risk Management (10 credits, semester 2)
Stochastic Optimization (5 credits, semester 2)

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

Visit the Computational Mathematical Finance (MSc) page on the University of Edinburgh website for more details!

Entry Requirements

The minimum entry requirement is a UK 2:1 degree, or its international equivalent, in mathematics or a mathematical subject such as statistics, physics or engineering. Relevant programming experience is considered essential.

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Recipient: University of Edinburgh
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