Sorry! De informatie die je zoekt, is enkel beschikbaar in het Engels.
This programme is saved in My Study Choice.
Something went wrong with processing the request.
Something went wrong with processing the request.

Seminar on Extreme Value Theory and Application

13 March 2023
On Thursday 30 March Vrije Universiteit Amsterdam will host the upcoming edition of the seminar series Extreme Value Theory and Application (EVTA).

Four Dutch universities jointly organize this quarterly seminar series that aims at inviting researchers in the field of Extreme Value Analysis from abroad.

The intended speaker from abroad, Stéphane Girard (Centre Inria Grenoble Rhône-Alpes), unfortunately had to cancel his trip to Amsterdam, because of the strikes in France. Yi He (University of Amsterdam) will then be the first speaker and he will give a talk on Rethinking tail inference. 

The domestic speaker is Annika Betken (University of Twente). Her presentation is entitled "Detecting structural changes in the tail-index of long memory stochastic volatility time series".

The abstracts of both presentations are at the end of this post.

The seminar is from 12.00 till 14.00 (lunch included) in the Main Building, De Boelelaan 1105, 1081 HV Amsterdam.

The four Dutch universities collaborating in the EVTA seminar series are: Erasmus University Rotterdam, Vrije Universiteit Amsterdam, Tilburg University and University of Amsterdam. More information can be found on the EVTA website.

If you are interested in joining this seminar, please send an email to the secretariat of the Department of Econometrics and Data Science at


"Rethinking Tail Inference" by Ye Hi
I will discuss co-authored papers that explore different origins of power laws and demonstrate how they impact the asymptotics of extreme value statistics. Furthermore, I will highlight the interconnected nature of heavy tail phenomena with other important phenomena encountered in economics and finance, such as heterogeneity and approximate sparsity. Recognizing these relationships can enable us to develop more comprehensive solutions to address the challenges of tail inference.

"Detecting structural changes in the tail-index of long memory stochastic volatility time series" by Annika Betken
We consider a change-point test based on the Hill estimator to test for structural changes in the tail index of long-memory stochastic volatility (LMSV) time series. In order to determine the asymptotic distribution of the corresponding test statistic, we prove a uniform reduction principle for the tail empirical process in a two-parameter Skorohod space. It is shown that such a process displays a dichotomous behavior according to an interplay between the Hurst parameter, i.e. a parameter characterizing the dependence in the data, and the tail index. We will see that, nonetheless, long-memory does not have an influence on the asymptotic behavior of the test statistic.