STK9120 – Extreme Value Theory
Course description
Course content
The course will give an overview of a number of different topics in modern extreme value theory including: Univariate extreme value theory, threshold models, point process characterization of extremes, maximum-likelihood estimation, peaks over thresholds, Hill-type estimation, multivariate extremes and extremes of processes, analysing heavy-tailed data in insurance and finance, Risk Management (enhancing Value-at-Risk). Software like Splus and R will be used.
Learning outcome
Extremal events appear in very different areas such as insurance, finance, telecommunications, hydrology, meteorology, and engineering, and make daily headlines in the media. The course gives an introduction to the basics of modern extreme value theory and extreme value statistics, which enables you to model extremes in these areas. Those include the basic limit theorems with the generalized extreme value distribution and the generalized extreme Pareto distribution in the limit. You will learn exploratory statistical tools for discovering the distribution of extremes and also be provided more sophisticated tools for the estimation of distribution tails and higher order quantiles of distributions. We also touch upon the modelling of extremes in time series (dependence over time) and for multivariate data (spatial dependency).
Admission
PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.
If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.
PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.
Prerequisites
Recommended previous knowledge
STK1100 – Probability and Statistical Modelling, STK1110 – Statistical Methods and Data Analysis, STK2120 – Statistical Methods and Data Analysis 2 (discontinued).
Overlapping courses
10 credits overlap with STK4120 – Extreme value theory (discontinued)
The information about overlaps is not complete. Contact the department for more information if necessary.
Teaching
3 hours of lectures/exercises per week.
If few students apply for the course, it may be given as self-tuition with one hour of common academic supervision each week.
Examination
Depending on the number of students, the exam will be in one of the following four forms:
1.Only written exam
2.Only oral exam
3.A project paper followed by a written exam.
4.A project paper followed by an oral exam/hearing.
For the latter two the project paper and the exam counts equally and the final grade is based on å general impression after the final exam. (The two parts of the exam will not be indivdually graded.)
What form the exam will take will be announced by the teaching staff within October 15th for the autumn semester and March 15th for the spring semester.
Examination support material
Permitted aids at the exam if written: Approved calculator.
Oral exam: no aids permitted
Information about approved calculators (Norwegian only)
Language of examination
Subjects taught in English will only offer the exam paper in English.
You may write your examination paper in Norwegian, Swedish, Danish or English.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
Explanations and appeals
Resit an examination
This course offers both postponed and resit of examination. Read more:
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
Evaluation
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.