MA323      Half Unit
Computational Methods in Financial Mathematics

This information is for the 2020/21 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; variance reduction techniques for MC simulation and related issues; numerical solutions to stochastic differential equations by means of MC simulation and their implementation; finite-difference schemes for the solution of partial differential equations arising in finance.


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. 

Important information in response to COVID-19

Please note that during 2020/21 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 situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of 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 2019/20: 30

Average class size 2019/20: 15

Capped 2019/20: 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