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2013-01-02Buch DOI: 10.18452/4441
Functional Data Analysis of Generalized Quantile Regressions
dc.contributor.authorGuo, Mengmeng
dc.contributor.authorZhou, Lhan
dc.contributor.authorHuang, Jianhua Z.
dc.contributor.authorHärdle, Wolfgang Karl
dc.date.accessioned2017-06-16T00:46:38Z
dc.date.available2017-06-16T00:46:38Z
dc.date.created2013-01-28
dc.date.issued2013-01-02
dc.date.submitted2013-01-02
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/5093
dc.description.abstractGeneralized quantile regressions, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized quantile regressions. Our approach assumes that the generalized quantile regressions share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized quantile regressions usually suffers from large variability due to lack of suffcient data, by borrowing strength across data sets, our joint estimation approach signifcantly improves the estimation effciency, which is demonstrated in a simulation study. The proposed method is applied to data from 150 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. These curves are needed to adjust temperature risk factors so that gaussianity is achieved. The normal distribution of temperature variations is vital for pricing weather derivatives with tools from mathematical finance.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectAsymmetric loss functioneng
dc.subjectCommon structureeng
dc.subjectFunctional data analysiseng
dc.subjectGeneralized quantile curveeng
dc.subjectIteratively reweighted least squareseng
dc.subjectPenalizationeng
dc.subject.ddc310 Statistik
dc.subject.ddc330 Wirtschaft
dc.titleFunctional Data Analysis of Generalized Quantile Regressions
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100206917
dc.identifier.doihttp://dx.doi.org/10.18452/4441
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages26
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2013
local.edoc.container-issue1
local.edoc.container-year2013
local.edoc.container-erstkatid2195055-6

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