ST409      Half Unit
Stochastic Processes

This information is for the 2024/25 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 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

(2020/21 - 2022/23 combined)

Classification % of students
Distinction 13.2
Merit 29.1
Pass 44.1
Fail 13.7

Key facts

Department: Statistics

Total students 2023/24: 68

Average class size 2023/24: 33

Controlled access 2023/24: No

Value: Half Unit

Guidelines for interpreting course guide information

Course selection videos

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Personal development skills

  • Team working
  • Problem solving
  • Application of information skills
  • Application of numeracy skills
  • Specialist skills