MA417 Half Unit
Computational Methods in Finance
This information is for the 2021/22 session.
Teacher responsible
Prof Luitgard Veraart
Availability
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.
Pre-requisites
Students must have completed September Introductory Course (Financial Mathematics and Quantitative Methods for Risk Management) (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 Lent Term.This year, apart from pre-recorded lecture videos, there will be a weekly live online session of an hour. Depending on circumstances, seminars might be online.
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 the ST.
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: 31
Average class size 2020/21: 31
Controlled access 2020/21: No
Value: Half Unit
Personal development skills
- Self-management
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Specialist skills
