MSc Computational Mathematical Finance
Structure and course options for the Computational Mathematical Finance MSc programme.
If you are studying full-time, you will take 120 credits of courses in total during Semesters 1 and 2, followed by a 60 credit dissertation which you complete over the summer.
If you are studying part-time, you will take 60 credits of courses in Year 1 of your programme, followed by 60 credits of courses in Year 2. You will then complete a 60 credit dissertation over the summer at the end of Year 2.
You will have the option to study one of three "streams": the Computational stream, the Financial stream, or the Machine Learning stream. Each stream features different sets of compulsory and optional courses.
The courses you take will be dependent on the availability of courses each year which may be subject to change as the curriculum develops to reflect a modern degree programme.
The following list of courses are compulsory for all streams of the Computational Mathematical Finance MSc. All courses are worth 10 credits, unless otherwise indicated.
Semester 1 compulsory courses have previously included:
- Discrete-Time Finance
- Python Programming
- Stochastic Analysis in Finance (20 credits)
Semester 2 compulsory courses have previously included:
- Numerical Probability and Monte Carlo
- Risk-Neutral Asset Pricing
- Stochastic Control and Dynamic Asset Allocation
- Research Skills for Financial Mathematics
All stream-specific compulsory courses are studied in Semester 2. All courses are worth 10 credits, unless otherwise indicated.
Computational stream compulsory courses have previously included:
- Time Series
- Numerical Partial Differential Equations
Financial stream compulsory courses have previously included:
- Financial Risk Theory
- Optimization Methods in Finance
Machine Learning stream compulsory courses have previously included:
- Machine Learning in Python
All courses are worth 10 credits, unless otherwise indicated.
Semester 1 optional courses have previously included:
- Blockchains and Distributed Ledgers
- Programming Skills
- Finance, Risk and Uncertainty
- Bayesian Theory
Semester 2 optional courses have previously included:
- 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
Over the summer, you will undertake work on a project in an approved area of financial mathematics. The aim of this project is to give an opportunity to apply the skills developed earlier in the programme, to work on a longer piece of work, and to deepen understanding in a particular area of mathematical finance. You will be required to write a dissertation based on the work that you conduct, communicating your results to other financial mathematicians.
Part-time study is available for working professionals who wish to enhance their career by pursuing an MSc degree whilst in employment. The 180 credits required are obtained over the course of two years with the final MSc project worth 60 credits taking place in the summer of the second year. The part-time mode of study comprises of the same compulsory and optional courses as the full-time mode.
Each 10 credit course consists of roughly 30 hours of lectures/labs/workshops with a further 70 hours of self-study (problem solving, reading, revision). Students need to obtain 120 credits during four semesters (two each year). Each semester is 12 weeks long, thus there are roughly 7.5 hours of lectures/workshops /labs each week during semesters that part-time students are expected to attend. All lectures are recorded, and all other teaching activities take place Monday-Friday between 9am and 6pm.
Effort is made, during timetabling, to ensure that each course has activities only on one day a week to enable part-time students to make arrangements to attend the courses as needed. If you wish to study part-time over two years it is recommended that you discuss the practicality of doing so with the Academic Selector by contacting them on futurestudents@ed.ac.uk.