MA417      Half Unit
Computational Methods in Finance

This information is for the 2024/25 session.

Teacher responsible

Prof Luitgard Veraart

Availability

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

The only programmes on which this course is available as an optional course are MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). 

Pre-requisites

Students must have completed September Introductory Course (Financial Mathematics and Quantitative Methods for Risk Management) (MA400).

Any students who are taking MA417 as an optional course and who have not completed MA400 need to obtain permission from the lecturer. They need to provide a statement explaining why and how they know the material covered in MA400.

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.

Teaching

This course is delivered through a combination of seminars and lectures totalling a minimum of 30 hours across Autumn Term. 

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; 

Assessment

Project (100%) in December.

Key facts

Department: Mathematics

Total students 2023/24: 58

Average class size 2023/24: 58

Controlled access 2023/24: No

Value: Half Unit

Guidelines for interpreting course guide information

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.

Personal development skills

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