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Home | Events Archive | Extreme Value Theory and Application Seminar​
Seminar

Extreme Value Theory and Application Seminar​


  • Series
    Econometrics Seminars and Workshop Series
  • Speaker(s)
    Phyllis Wan , Stéphane Girard (Inria Centre, University Grenoble Alpes, France)
  • Field
    Econometrics, Data Science
  • Location
    University of Amsterdam, Nieuwe Achtergracht 166, C0.02 at Building C
    Amsterdam
  • Date and time

    December 01, 2023
    11:00 - 12:50

11:00-11:45 Phyllis Wan (Erasmus University Rotterdam) Title: Parametric and Non-parametric Estimation of Extreme Earthquake Event Abstract: In an earthquake event, the combination of a strong mainshock and damaging aftershocks is often the cause of severe structural damages and/or high death tolls. The objective of this paper is to provide estimation for the probability of such extreme events where the mainshock and the largest aftershocks exceed certain thresholds. Two approaches are illustrated and compared – a parametric approach based on previously observed stochastic laws in earthquake data, and a non-parametric approach based on bivariate extreme value theory. We analyze the earthquake data from the North Anatolian Fault Zone (NAFZ) in Turkey during 1965–2018 and show that the two approaches provide unifying results. Joint work with Juan-Juan Cai. 11:45-11:50 Break 11:50-12:50 Stephane Girard (Université Grenoble Alpes) Title: Simulation of Extreme Values With Neural Networks Abstract: Neural networks based on Rectified linear units (ReLU) cannot efficiently approximate quantile functions which are not bounded, especially in the case of heavy-tailed distributions. We thus propose a new parametrization for the generator of a Generative adversarial network (GAN) adapted to this framework, basing on extreme-value theory. An analysis of the uniform error between the extreme quantile and its GAN approximation is provided: We establish that the rate of convergence of the error is mainly driven by the second-order parameter of the data distribution. The above results are illustrated on simulated data and real financial data. It appears that our approach outperforms the classical GAN in a wide range of situations including high-dimensional and dependent data. Joint work with Michaël Allouche and Emmanuel Gobet (Ecole Polytechnique, France). Lunch at Room E5.22: 13:00-14:00