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Statistics and Computational Finance MSc

Statistics and Computational Finance MSc

Key information

Qualification type

MSc - Master of Science

Subject areas

Computational Mathematics / Cybernetics Statistics Financial Modelling Finance / Accounting (General)

Course type


Course Summary

Our course emphasises data analysis and will provide you with contemporary statistical ideas and methodologies that are attractive to prospective employers. The skills you gain are useful for a wide range of financial data analysis and in a range of other sectors where data analysis is required, for example sociology, health science, medical science or biology. This experience is also an ideal foundation for further academic study; many of our students choose to progress to PhD. This course will equip you with the necessary skills to: translate problems from the workplace into contemporary statistical ideas and methodologies; solve problems using your advanced knowledge in statistical modelling and computational finance; interpret and communicate your results. All taught modules are assessed by a combination of closed book written exams, coursework, projects and presentations. The closed book written exam assesses your subject-specific knowledge through both theoretical and practical questions and open-ended problems. The coursework and projects often require the use of software, giving you an opportunity to develop your technical skills. They will test your subject knowledge and analytical, theoretical skills as well as the practical aspects of application, implementation and interpretation. The big data analysis skills you develop on this course provide attractive employment opportunities in a growing number of industries where such skills are in high demand. The course is also a good foundation for continuing your studies at PhD level.

Different course options

Study mode

Full time


1 year

Start date



This module enables students to acquire in-depth knowledge of the main features of Ito stochastic calculus as applied in mathematical finance, including: The role of the Ito integral and Ito formula in solving stochastic differential equations (SDEs); Martingale properties of the Ito integral and the structure of Brownian martingales; The mathematical relationships between wealth processes, investment strategies and option prices; Change of measure techniques and Girsanov theorem; Partial differential equation (PDE) approach, and in particular the Black-Scholes equation; Feynman-Kac representation of option prices. The emphasis is on fundamental concepts which underlie the main continuous-time models of option pricing, principally the Black-Scholes model. Both plain vanilla (European) and exotic options (for example, barrier options) are dealt with, and the relationship between the approaches based on martingale theory and partial differential equations is explored. The module aims to equip students with a thorough understanding of the sophisticated mathematical results and techniques encountered in financial market modelling.

Tuition fees

UK fees
Course fees for UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


International fees
Course fees for non-UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


Entry requirements

Applicants should have, or be about to complete, a 2:1 undergraduate degree in mathematics or in a subject with a substantial mathematics component. We may accept a 2:2 undergraduate degree supported by relevant professional qualifications.