Teaching & modules
Modules
Year 1
Required Modules
For both full-time and part-time MSc:
- Probability Theory (15 credits)
- Risk-Neutral Valuation: Pricing and Hedging Derivatives (15 credits)
For full-time MSc only:
- MSc Financial Mathematics Project (60 credits)
Other supporting module for both full-time and part-time MSc:
Mathematical Analysis for Financial Mathematics (0 credits)
Optional Modules
Additionally, you are required to take 90 credits (for full-time MSc) and 30 credits (for part-time MSc) from a range of optional modules, which may typically include:
- Financial Markets (15 credits)
- Statistics in Finance (15 credits)
- Stochastic Analysis (15 credits)
- C++ for Financial Mathematics (15 credits)
- Interest Rates & Credit Risk (15 credits)
- Econophysics (15 credits)
- Numerical & Computational Methods in Finance (15 credits)
- Elements of Statistical Learning (15 credits)
- Scientific Computing for Finance (15 credits)
- High-Frequency Finance (15 credits)
- Stochastic Control and Applications to Algorithmic Trading (15 credits)
- Incomplete Markets (15 credits)
- Computational Statistics (15 credits)
- Time Series Analysis (15 credits)
- Statistics for Data Analysis (15 credits)
- Machine Learning (15 credits)
Year 2 - For part-time MSc only
Required Modules
- MSc Financial Mathematics Project (60 credits)
Optional Modules
Additionally, you are required to take 60 credits from a range of optional modules which may typically include:
- Financial Markets (15 credits)
- Statistics in Finance (15 credits)
- Stochastic Analysis (15 credits)
- C++ for Financial Mathematics (15 credits)
- Interest Rates & Credit Risk (15 credits)
- Econophysics (15 credits)
- Numerical & Computational Methods in Finance (15 credits)
- Elements of Statistical Learning (15 credits)
- Scientific Computing for Finance (15 credits)
- High-Frequency Finance (15 credits)
- Stochastic Control and Applications to Algorithmic Trading (15 credits)
- Incomplete Markets (15 credits)
- Computational Statistics (15 credits)
- Time Series Analysis (15 credits)
- Statistics for Data Analysis (15 credits)
- Machine Learning (15 credits)
Teaching methods - what to expect
We use lectures and group tutorials to deliver most of the modules on the course. You will also be expected to undertake a significant amount of independent study.
Typically, one credit equates to 10 hours of work, e.g. 150 hours work for a 15-credit module. These hours cover every aspect of the module.
We will use a delivery method that will ensure students have a rich, exciting experience from the start. Face to face teaching will be complemented and supported with innovative technology so that students also experience elements of digital learning and assessment.
At King’s, all students are allocated a Personal Tutor who will play a key role in helping you to get the most out of your studies, providing support and encouragement for your time at university. Personal tutors provide you with the opportunity to periodically take stock of your learning, academic progress and general wellbeing as you progress through your studies, offering guidance on how to seek further support if you need it, and how to access the range of opportunities available to you as a King's student.
The majority of learning for this degree takes place at the King’s College London Strand Campus, with occasional lectures and practical sessions taking place at the Waterloo Campus. Please note that locations are determined by where each module is taught and may vary depending on the optional modules you select.
Assessment
- Course Work
- Written/practical examinations
- Unseen written examinations
- Class tests
- Online quizzes
Your performance will be assessed through a combination of coursework and written/practical examinations. Forms of assessment may typically include unseen written exams, class tests, online quizzes and coursework submission.
Application closing date guidance
Key Information
Course type:
Master's
Delivery mode:
In person
Study mode:
Full time / Part time
Duration:
One year full-time, two years part-time
Application status:
Open
Start date:
September 2026