September Introductory Course (Financial Mathematics and Quantitative Methods for Risk Management)

This information is for the 2019/20 session.

Teacher responsible

Dr Arne Lokka and Prof Johannes Ruf


This course is compulsory on the MSc in Financial Mathematics and MSc in Quantitative Methods for Risk Management. This course is available with permission as an outside option to students on other programmes where regulations permit.

Students who wish to select this course as an outside option must have a quantitative background.

Course content

The purpose of this course is to review some key concepts of probability used in finance. The course develops the common mathematical background that is assumed by the MSc Financial Mathematics and addresses some aspects of the mathematical theory that is central to the foundations of the programme: probability spaces, random variables, distributions, expectations and moment generating functions are reviewed; the concepts of conditional probability and conditional expectation as random variables are introduced using intuitive arguments and simple examples; stochastic processes, martingales, the standard Brownian motion are introduced; Itô integrals, Itô's formula and Girsanov's theorem are discussed on a formal basis.


30 lectures and 6 classes over two weeks during September, prior to the start of the academic year. There will be an informal examination.

Formative coursework

Exercises are assigned and form the basis of class discussion.

Indicative reading

Lecture notes will be provided.

S. Shreve, Stochastic Calculus for Finance II Continuous-Time Models, Springer.

D. Williams, Probability with Martingales, Cambridge University Press.


This course does not form part of the degree award.

Key facts

Department: Mathematics

Total students 2018/19: 65

Average class size 2018/19: Unavailable

Controlled access 2018/19: No

Value: Non-credit bearing

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of numeracy skills
  • Specialist skills