MA323 Half Unit
Computational Methods in Financial Mathematics
This information is for the 2023/24 session.
Teacher responsible
Dr Christoph Czichowsky
Availability
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.
Pre-requisites
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.
Teaching
This course is delivered through a combination of classes and lectures totalling a minimum of 30 hours across Autumn Term. This year, some of the 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 WT.
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).
Assessment
Project (100%) in the ST.
The project will be a computational project due to in the week before ST starts.
Key facts
Department: Mathematics
Total students 2022/23: 30
Average class size 2022/23: 30
Capped 2022/23: No
Lecture capture used 2022/23: Yes (LT)
Value: Half Unit
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