MA417      Half Unit
Computational Methods in Finance

This information is for the 2019/20 session.

Teacher responsible

Dr Luitgard Veraart


This course is compulsory on the MSc in Financial Mathematics. This course is available with permission as an outside option to students on other programmes where regulations permit.


Students must have completed September Introductory Course (Financial Mathematics and Quantitative Methods for Risk Management) (MA400) and Programming in C++ (MA432).

Course content

The purpose of this course is to (a) develop the students' computational skills, and (b) introduce a range of numerical techniques of importance to financial engineering. The course starts with random number generation, the fundamentals of Monte Carlo simulation and a number of related issues. Numerical solutions to stochastic differential equations and their implementation are considered. The course then addresses finite-difference schemes for the solution of partial differential equations arising in finance.


20 hours of lectures, 4 hours of seminars and 10 hours of computer workshops in the LT.

Formative coursework

Weekly exercises and practicals are set and form the basis of the seminars.

Indicative reading

P.Glasserman, Monte Carlo Methods in Financial Engineering, Springer; R.U. Seydel, Tools for Computational Finance, Springer; P.E.Kloeden and E.Platen, Numerical Solution of Stochastic Differential Equations, Springer; D.M. Capper, Introducing C++ for Scientists, Engineers and Mathematicians, Springer. B. Stroustrup, The C++ Programming Language, Addison Wesley; M. J. Capinski, T. Zastawniak, Numerical Methods in Finance with C++, Cambridge University Press; M. S. Joshi, C++ Design Patterns and Derivatives Pricing, Cambridge University Press;


Exam (50%, duration: 2 hours) in the summer exam period.
Project (50%) in the ST.

Key facts

Department: Mathematics

Total students 2018/19: 31

Average class size 2018/19: 31

Controlled access 2018/19: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills