ST803: Extreme Value Statistics

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N370002102
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master's level course approved as PhD course

STADS ID (UVA): N370002101
ECTS value: 5

Date of Approval: 25-10-2018

Duration: 1 semester

Version: Approved - active


25001901(former UVA) is identical with this course description. 

Entry requirements


Academic preconditions

Students taking the course are expected to:
  • Have knowledge of mathematical statistics and probability theory

Course introduction

The aim of the course is to enable the student to work in a
rigorous way with probability models for extreme values, which is
important in regard to modelling of extreme events, e.g. in finance and

The course builds on the knowledge acquired
in the courses ST521 Mathematical Statistics and MM544 Probability
Theory, and gives an academic basis for master thesis projects in
extreme values.

In relation to the competence profile of the degree it is the explicit focus of the course to:

  • Give the competence to handle model building and model calculations¬†
  • Give skills to perform statistical analysis
  • Give theoretical knowledge and practical experience with the application of methods and models from statistics

Expected learning outcome

The learning objectives of the course is that the student demonstrates the ability to:
  • reproduce the theoretical results concerning the convergence of maxima and excesses over a threshold
  • verify if a distribution is in the domain of attraction of the generalized extreme value distribution
  • verify if a distribution function satisfies the second order condition on tail behavior
  • describe
    the principles of tail index estimation and extreme quantile
    estimation, and to apply these in a given practical setting
  • perform programming relevant to the content of the course in the statistical package used in the course
  • identify and interpret relevant information in the output of the statistical package used in the course
  • summarize the results of an extreme value analysis in a statistical report


The following main topics are contained in the course:
Graphical tools for tail analysis, order statistics, convergence of normalized sample maxima, domain of attraction of the generalized extreme value distribution, convergence of excesses over thresholds, the generalized Pareto distribution, second order tail behavior, estimation of the tail index, extremes in regression analysis.


See Blackboard for syllabus lists and additional literature references.

Examination regulations

Exam element a)




Oral exam




Second examiner: Internal


7-point grading scale


Student Identification Card


Normally, the same as teaching language

Examination aids

No exam aids allowed, a closer description of the exam rules will be posted under 'Course Information' on Blackboard.

ECTS value


Additional information

The oral exam is based on but not limited to a project assignment.

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

40 hours per semester

Teaching Method

In the intro phase a modified version of the classical
lecture is employed, where the terms and concepts of the topic are
presented, from theory as well as from examples based on actual data. In
these hours there is room for questions and discussions.
In the training phase
the students work with data-based problems and discussion topics,
related to the content of the previous lectures in the intro phase. In
these hours there is a possibility of working specifically with selected
difficult concepts.
In the study phase the
students work independently with problems and the understanding of the
terms and concepts of the topic. Questions from the study phase can
afterwards be presented in either the intro phase hours or the training
phase hours.

Educational activities 

  • Studying the course material and preparing the weekly exercises, individually or through group work

Teacher responsible

Name E-mail Department
Yuri Goegebeur Institut for Matematik og Datalogi, Statistik


Administrative Unit

Institut for Matematik og Datalogi (matematik)

Team at Registration & Legality


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