Computational Finance (AAM)

Course content

See "Knowledge" below. 


MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics

Learning outcome

Knowledge (= a rough lecture plan)

  • Rudimentary low-level programming.
  • Data and computational resources at Copenhagen University and beyond.
  • Monte Carlo simulation techniques in option pricing: Variance reduction, diffusion (and possibly Levy) process simulation, American options, adjoint techniques.
  • Numerical transform methods for option pricing.
  • Numerical optimization and model calibration.
  • Numerical methods for solving parabolic partial differential equations. 


Only a selection (based on lecturer and student interest) of the last three topics will be covered.



High- and low-level programming as fits the problem.

Extracting and handling financial data.

Effcient use computaional resources, both wrt. hardware (distributed computing) and software (error order analysis).

Ability to implement Monte Carlo simulation techniques (to investigate pricing and hedging) for a large range of financial products and models.

Ability to implement a (limited) number of more specialized methods for more specific models and problems.



Proficieny classical and modern numerical methods for quantitative finance problems. This is a question of having both a sizeable "toolbox" and the ability pick the appropriate on in a given situation. 

4 hours of lectures and and 2 hours of exercises per week for 9 weeks.

See Absalon for a list of course literature.

A bachelor degree from the Departments of Mathematical Sciences (or something suitably close to that; plus (at least) working knowledge of continuous-time finance.

7,5 ECTS
Type of assessment
Continuous assessment
3 equally weighted hand-ins over the course of the course.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner.
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 36
  • Theory exercises
  • 18
  • Project work
  • 76
  • Preparation
  • 76
  • English
  • 206