ST433 Half Unit
Computational Methods in Finance and Insurance
This information is for the 2020/21 session.
Teacher responsible
Dr Debora Escobar COL7.13
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 and lectures totalling a minimum of 32 hours across Lent Term. This year, some or all 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
Exam (50%, duration: 2 hours) in the summer exam period.
Project (50%) in the ST.
Student performance results
(2016/17 - 2018/19 combined)
| Classification | % of students |
|---|---|
| Distinction | 36.4 |
| Merit | 35.5 |
| Pass | 13.1 |
| Fail | 15 |
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: Statistics
Total students 2019/20: 25
Average class size 2019/20: 25
Controlled access 2019/20: No
Value: Half Unit
Personal development skills
- Team working
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Commercial awareness
- Specialist skills
