ST433 Half Unit
Computational Methods in Finance and Insurance
This information is for the 2021/22 session.
Teacher responsible
Mr Yufei Zhang
Availability
This course is compulsory on the MSc in Quantitative Methods for Risk Management. This course is available on the MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan) and MSc in Statistics (Financial Statistics) (Research). 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, (b) introduce a range of numerical techniques of importance in actuarial and financial engineering, and (c) develop the ability of the students to apply the theory from the taught courses to practical problems, work out solutions including numerical work, and to present the results in a written report.
Binomial and trinomial trees. Random number generation, the fundamentals of Monte Carlo simulation and a number of related issues. Finite difference schemes for the solution of ordinary and partial differential equations arising in insurance and finance. Numerical solutions to stochastic differential equations and their implementation. The course ends with an introduction to guidelines for writing a scholarly report/thesis.
Teaching
This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 32 hours across Lent Term. This year, some of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos.
Formative coursework
Weekly exercises and practicals are set and form the basis of the classes.
Indicative reading
N E Steenrod, P Halmos, M M Schiffer & J A Dieudonne, How to write mathematics (1973); D.J. Duffy, Finite Difference Methods in Financial Engineering: A Partial Differential Equation Approach, Wiley; P. Glasserman, MonteCarlo Methods in Financial Engineering, Springer; P.E. Kloden and E. Platen, Numerical Solution of Stochastic Differential Equations, Springer. Further material will be specified during the course.
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.
Student performance results
(2017/18 - 2019/20 combined)
| Classification | % of students |
|---|---|
| Distinction | 39.6 |
| Merit | 33.7 |
| Pass | 17.8 |
| Fail | 8.9 |
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: Statistics
Total students 2020/21: 37
Average class size 2020/21: 38
Controlled access 2020/21: Yes
Value: Half Unit
Personal development skills
- Team working
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Commercial awareness
- Specialist skills
