ST409 Half Unit
Stochastic Processes
This information is for the 2023/24 session.
Teacher responsible
Dr Andreas Sojmark COL 7.04
Availability
This course is compulsory on the MSc in Financial Mathematics and MSc in Quantitative Methods for Risk Management. This course is available on the MSc in Applicable Mathematics, MSc in Econometrics and Mathematical Economics, MSc in Operations Research & Analytics, MSc in Risk and Finance, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.
This course has a limited number of places (it is controlled access) and demand is typically very high. Students for whom the course is not compulsory and who meet the necessary pre-requisites may be allocated a place, space permitting. Students must provide a statement explaining how they meet the pre-requisites when asking for a place.
Pre-requisites
Students must have completed Further Mathematical Methods (MA212).
Good undergraduate knowledge of distribution theory
Course content
A broad introduction to stochastic processes for postgraduates with an emphasis on financial and actuarial applications. The course examines Martingales, Poisson Processes, Brownian motion, stochastic differential equations and diffusion processes. Applications in Finance. Actuarial applications.
Teaching
This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across Michaelmas Term. This course includes a reading week in Week 6 of Michaelmas Term.
Indicative reading
T Bjork, Arbitrage Theory in Continuous Time; T Mikosch, Elementary Stochastic Calculus; S I Resnick, Adventures in Stochastic Processes; B K Oksendal, Stochastic Differential Equations: An Introduction with Applications, D Williams, Probability with Martingales.
Assessment
Exam (100%, duration: 2 hours) in the spring exam period.
Student performance results
(2019/20 - 2021/22 combined)
Classification | % of students |
---|---|
Distinction | 30.1 |
Merit | 26.5 |
Pass | 33.3 |
Fail | 10 |
Key facts
Department: Statistics
Total students 2022/23: 79
Average class size 2022/23: 40
Controlled access 2022/23: Yes
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
- Team working
- Problem solving
- Application of information skills
- Application of numeracy skills
- Specialist skills