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Course Structure - MSc in Mathematical Finance

The MSc in Mathematical Finance builds on your mathematical background to equip you with knowledge of probability and stochastic processes, statistics, numerical methods, derivatives and asset pricing to give you the tools to study more advanced topics such as interest rate models and credit risk, model calibration, financial time series, risk management and PDE's. You will learn to programme in Python, Matlab or R and undertake a module in C++ with financial applications.

The programme culminates in a research project/dissertation which allows you to study a subject of interest to you in greater detail.

The current course structure is:

Induction Week

Fundamental Tools: this is a refresher course, covering mathematical concepts essential to the programme.

Term 1
: Core Modules

MA907 Simulation and Machine Learning for Finance (Mathematics)

ST959 Financial Statistics (Statistics)

ST908 Stochastic Calculus for Finance (Statistics)

IB9110 Asset Pricing and Risk (WBS)

IB9JH0 Programming for Quantitative Finance (WBS)

Term 2: Core Modules and Electives

ST909 Applications of Stochastic Calculus for Finance (Statistics)

IB9JH0 Programming for Quantitative Finance (WBS) (continued from Term 1)

IB9KC Financial Econometrics (WBS)

plus two Electives, see Lists A and B below.

Term 3 and Summer:

ST915 Dissertation


List A: students must choose at least one from this list:

ST420 Statistical Learning and Big Data (Statistics)

ST958 Advanced Trading Strategies (Statistics)

MA908 Partial Differential Equations for Finance (Maths)

List B: students can take no more than 1 from this list but may take none.

ST403: Brownian Motion (Statistics)

IB9Y20: Behavioural Finance (WBS) 

IB9CR0: Alternative Investments (WBS) 


Your dissertation is a chance to research a topic of interest to you in further depth. Recent dissertation titles in the field include: (department supervising in brackets)

  • Change Point Detection in Financial Time Series (Statistics)
  • Levy processes and the pricing of path-dependent options (Statistics)
  • Optimism and Stock Trading (Statistics)
  • What are the implications of the EMH for Mathematical Modelling of Financial Markets? (Statistics)
  • Optimal Strategies for High-Frequency Trading (Maths)
  • Spark Spread options for Bitcoin Miners (Maths)
  • Multilevel Monte Carlo Path Simulations (Maths)
  • Modelling Systemic Risk (WBS)
  • Estimating Multiple Option Gammas via Simulation (WBS)

Exam Periods

Term 1 modules are examined in week 1 of Term 2. Term 2 modules are examined in weeks 1-3 of Term 3. See the university website for term dates.

The dissertation starts after the Term 3 exam period and is normally due in the first week of September.