Computational Finance: Calibration and Estimation

5 credits

Syllabus, Master's level, 1TD188

Education cycle
Second cycle
Main field(s) of study and in-depth level
Computational Science A1N, Computer Science A1N, Financial Mathematics A1N
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 30 August 2018
Responsible department
Department of Information Technology

Entry requirements

Scientific Computing II or Scientific Computing, Bridging Course, 5 credits, and one course in mathematical statistics (at least 5 credits). Financial Derivatives is recommended. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

  • describe and implement solution methodologies and optimisation methods for calibration of interest rate models and calibration of a single underlier model;
  • describe and implement solution methodologies for filtering and parameter estimation in financial models;
  • use advanced software for calibration of models and parameter estimation for financial applications;
  • appraise, interpret and discuss computational results orally and in a written report;
  • summarise a scientific paper in the area.


The course contains areas which are essential when practically dealing with computational methods in finance in engineering and research. The content includes methods for calibration of stochastic models for financial applications, optimisation methods for calibration, filtering, maximum likelihood-estimation and Kalman-filter. The course contains general parts, which all participants take, as well as a number of eligible modules. Thus, the course can partly be individually adjusted. The software that is used is Front Arena and MATLAB.


Recorded web-based lectures, lectures, guest lectures, seminars, group supervision and laboratory work. Participants work in groups as well as on individual basis.


Projects/assignments presented in written reports and orally in seminars.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.