Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions

Ahmad Aboubacrène Ag, Deme El Hadji, Diop Aliou and Girard Stéphane ()
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Ahmad Aboubacrène Ag: Université Gaston Berger, LERSTAD, UFR SAT, BP 234, Saint-Louis, Sénégal
Deme El Hadji: Université Gaston Berger, LERSTAD, UFR SAT, BP 234, Saint-Louis, Sénégal
Diop Aliou: Université Gaston Berger, LERSTAD, UFR SAT, BP 234, Saint-Louis, Sénégal
Girard Stéphane: Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000Grenoble, France

Dependence Modeling, 2019, vol. 7, issue 1, 394-417

Abstract: We introduce a location-scale model for conditional heavy-tailed distributions when the covariate is deterministic. First, nonparametric estimators of the location and scale functions are introduced. Second, an estimator of the conditional extreme-value index is derived. The asymptotic properties of the estimators are established under mild assumptions and their finite sample properties are illustrated both on simulated and real data.

Keywords: Nonparametric estimation; location-scale function; tail-index; extreme-values; conditional quantile (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:7:y:2019:i:1:p:394-417:n:21

DOI: 10.1515/demo-2019-0021

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