Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Computational Finance (AAM)

Practical information
Study year 2016/2017
Block 1
Programme level Ph.D.
Course responsible
  • Rolf Poulsen (4-7774716b457266796d33707a336970)
  • Department of Mathematical Sciences
Course number: NSCPHD1078

Course content

See "Knowledge" below. 

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. 









Recommended prerequisites

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

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Natural Sciences PhD Committee

Course type

Single subject courses (day)


David Skovmand


1 block


---- SKEMA LINK ----

Teaching and learning methods

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


No restrictions/ no limitations




Notes, articles and working papers 


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


Type of assessment

Continuous assessment
3 hand-ins over the course of the course.


All aids allowed

Marking scale

7-point grading scale

Criteria for exam assessment

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

Censorship form

No external censorship


20 minute oral exam without preparation.

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