King's College London

Course Introduction

The Computational Finance MSc will introduce students to the computational methods that are widely used by practitioners and financial institutions in today?s markets. This course will provide students with a solid foundation not only in traditional quantitative methods and financial instruments, but also scientific computing, numerical methods, high-performance computing, distributed ledgers, big-data analytics and agent-based modelling. These techniques will be used to understand financial markets from a post-crisis perspective which incorporates findings from the study of financial markets at high-frequency time scales, modern approaches to understanding systematic risk and financial contagion, and disruptive technologies such as distributed-ledgers and crypto- currencies. The course is highly practical, and students will have the opportunity to apply their learning to real-world data and case studies in hands-on laboratory sessions.


Computational Finance MSc studies problems of optimal investment, risk management and trade execution from a computational perspective. As with any engineering discipline, computational finance analyses a given problem by first building a model for it and then examining the model.

In computational finance, however, our model is typically analysed by running computer programs, rather than solving mathematical equations. In addition to standard computational methods such as Monte-Carlo option pricing, you will also learn more advanced modelling techniques such as agent-based modelling, in which the model itself takes the form of a computer program.

The course will provide a foundation in the core skills required for successful risk management and optimal investment by giving a grounding in the key quantitative methods used in finance, including computer programming, numerical methods, scientific computing, numerical optimisation and an overview of the financial markets. You can then go on to study more advanced topics, including the market micro- structure of modern electronic exchanges, high-frequency finance, distributed-ledger technology and agent-based modelling.


Assessment methods will depend on the modules selected. The primary methods of assessment for this course are written examinations and coursework. You may also be assessed by class tests, essays, assessment reports and oral presentations.

Duration & Attendance Qualification Tuition fees Fee type
1 year
Full Time
MSc £28,500 Home Fees
1 year
Full Time
MSc £28,500 European Fees
1 year
Full Time
MSc £28,500 Overseas Fees

Qualifications required:

  • Domestic entry requirements: 2:1 undergraduate degree from a related quantitative or informatics discipline. In order to meet the academic entry requirements for this programme you should have a minimum 2:1 undergraduate degree with a final mark of at least 60% or above in the UK marking scheme. If you are still studying you should be achieving an average of at least 60% or above in the UK marking scheme. Applicants should also provide evidence of studying calculus, linear algebra, programming and other related modules.
  • International entry requirements: Students can demonstrate their English Language Proficiency with any of the following tests and grades: IELTS (Academic): 6.5 overall with a minimum of 6.0 in each skill; TOEFL IBT: 92 overall, with a minimum 23 in each skill; Cambridge Advanced Certificate (CAE): 176 overall with a minimum of 169 in each skill; Cambridge Certificate of Proficiency in English (CPE): 176 overall with a minimum of 169 in each skill; Pearson Test of English (Academic): 62 overall, with a minimum of 52 in each skill. The tests listed above are valid if they have been taken within the past two years.
  • IELTS score: 6.5
  • TOEFL score: 92