MA323      Half Unit
Computational Methods in Financial Mathematics

This information is for the 2021/22 session.

Teacher responsible

Prof Johannes Ruf


This course is compulsory on the BSc in Financial Mathematics and Statistics. This course is not available as an outside option. This course is available with permission to General Course students.


Students must have completed Introduction to Pricing, Hedging and Optimization (ST213).

Course content

Random number generation; the fundamentals of Monte Carlo (MC) simulation and applications in financial mathematics; variance reduction techniques for MC simulation and related issues; stochastic differential equations and their numerical solutions by means of MC simulation and their implementation.


This course is delivered through a combination of classes and lectures totalling a minimum of 30 hours across Lent Term. This year, some or all of this teaching will be delivered through a combination of virtual classes and lectures delivered as online videos. 

Formative coursework

Students will be expected to produce 5 problem sets and 5 other pieces of coursework in the LT.

Indicative reading

P. Glasserman, Monte Carlo Methods in Financial Engineering, Springer;

R.U. Seydel, Tools for Computational Finance, Springer;

S.M. Ross, Simulation, Academic Press (5th edition).


Project (100%) in the ST.

The project will be a computational project. 

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Mathematics

Total students 2020/21: 35

Average class size 2020/21: 12

Capped 2020/21: 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