Introduction to the course

Dear students,

Welcome to the course Foundations of Quantitative Risk Measurement for the academic year 2022 – 2023. My name is Daniël Linders and I will be the instructor for this course. This course will be hybrid with in-person lectures and pre-recorded video lectures. 

The main place to look for updates, slides, videos, etc. about this course is the Toledo website. However, if you have no access at the moment, you can find all updates on this website, for the moment. However, please make sure you register for Toledo!

Pease note that the course is designed with the assumption that students will attend the lectures at the KU Leuven (in-person) and watch the videos during specific time spans.

Your final grade will be solely based on the final exam, which will take place in January 20, 2023. However, additional bonus points can be earned during the course by participating on quizzes. These quizzes will be distributed randomly throughout the course and will take place before, during or after the different lectures. More information will be availble during the first lecture. In order to optimally benefit from the bonuspoints, physically attendingn the live lectures will be required

If you have any questions about this course, do not hesitate to contact me via email (daniel.linders@kuleuven.be).

Kind regards,

Daniël Linders


Course Schedule

  • Monday November 21: 9 – 11am in HOG 02.28
  • Monday November 21: 1 – 4pm in HOG 01.85
  • Monday November 28: 9 – 11am in HOG 02.28
  • Monday November 28: 1 – 4pm in HOG 01.85
  • Monday December 5: 9 – 11am in HOG 02.28
  • Monday December 5: 1 – 4pm in HOG 01.85
  • Monday December 12: 9 – 11am in HOG 02.28
  • Monday December 12: 1 – 4pm in HOG 01.85
  • Monday December 19: 9 – 11am in HOG 02.28
  • Monday December 19: 1 – 4pm in HOG 01.85

The full lecture schedule can be found here.


Course Documents

I will upload all material (slides, notes, exercises, etc) I use during the lecture here!

Chapter 1: Expected Utility Theory
Chapter 2: Integral Stochastic Orders
Chapter 3: Stochastic Dependence
Chapter 4: Comonotonicity
Chapter 5: Yaari’s Dual Theory
Chapter 6: Risk Measures
Chapter 7: Subadditivity and Risk Aggregation