Computational Finance

Course content

See "Knowledge" below. 


MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics

Learning outcome

Knowledge (= a rough lecture plan)


Topics may include but are not limited to:

  • High-level programming
  • Data and computational resources
  • Monte Carlo 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
  • Machine learning approaches in quantitative finance


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



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

Extracting and handling financial data.

Ability to implement numerical techniques (to investigate pricing and hedging) for a 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, and some experience with programming.

Academic qualifications equivalent to a BSc degree is recommended.

Continuous feedback during the course

Individual feedback given on the basis of assignments. 

7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
2 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
  • Preparation
  • 76
  • Theory exercises
  • 18
  • Project work
  • 76
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 4
No limit
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Nils Martin Anders Tegnér   (6-766769706774426f63766a306d7730666d)
Saved on the 28-02-2022

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