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SUMMARY:Discrete Multivariate Generalized Pareto Distribution for Drought 
 Risk Assessment
DTSTART:20251104T130000Z
DTEND:20251104T143000Z
DTSTAMP:20260424T125300Z
UID:indico-event-15414@indico.math.cnrs.fr
DESCRIPTION:Speakers: Marie Kratz (ESSEC Cergy)\n\nDroughts are among the 
 most severe and growing risks associated with climate change\, with major 
 consequences for society. Managing these risks requires contributions from
  multiple disciplines\, including climate sciences\, probability and stati
 stics. Our work contributes to this agenda by extending extreme value theo
 ry (EVT) to discrete multivariate settings\, through the introduction of M
 ultivariate Discrete Generalized Pareto Distributions (MDGPDs). These mode
 ls bridge the gap between continuous EVT and discrete count data\, offerin
 g a flexible approach to threshold exceedances for events such as dry spel
 ls. Rooted in Generalized Pareto theory\, MDGPDs provide a principled fram
 ework for representing rare and compound events in a variety of applied co
 ntexts. We present the theoretical construction of MDGPDs\, simulation met
 hods\, and likelihood-free inference techniques tailored to this discrete 
 multivariate framework. A case study on European drought events illustrate
 s the practical relevance of the model for climate-related risk assessment
 . The tools developed support decision-makers—such as insurers\, policym
 akers\, and climate risk analysts—in better understanding\, anticipating
 \, and pricing the impacts of extreme dry periods. This is a joint work wi
 th S. Aka (ESSEC CREAR & LSCE Saclay) and P. Naveau (LSCE Saclay).\n\nhttp
 s://indico.math.cnrs.fr/event/15414/
LOCATION:UTC GI
URL:https://indico.math.cnrs.fr/event/15414/
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