ST433      Half Unit
Computational Methods in Finance and Insurance

This information is for the 2019/20 session.

Teacher responsible

Prof Umut Cetin COL6.08


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.


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.


20 hours of lectures and 10 hours of workshops in the LT.

Week 6 will be used as a reading week.


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.


Exam (50%, duration: 2 hours) in the summer exam period.
Project (50%) in the ST.

Student performance results

(2015/16 - 2017/18 combined)

Classification % of students
Distinction 28.7
Merit 32.7
Pass 16.8
Fail 21.8

Key facts

Department: Statistics

Total students 2018/19: 35

Average class size 2018/19: 35

Controlled access 2018/19: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills