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dc.contributor.authorGuillén, Montserrat-
dc.contributor.authorBermúdez, Lluís-
dc.contributor.authorPitarque, Albert-
dc.description.abstractBased on recent developments in joint regression models for quantile and expected shortfall, this paper seeks to develop models to analyse the risk in the right tail of the distribution of non-negative dependent random variables. We propose an algorithm to estimate conditional tail expectation regressions, introducing generalized risk regression models with link functions that are similar to those in generalized linear models. To preserve the natural ordering of risk measures conditional on a set of covariates, we add extra non-negative terms to the quantile regression. A case using telematics data in motor insurance illustrates the practical implementation of predictive risk models and their potential usefulness in actuarial analysis.-
dc.format.extent8 p.-
dc.publisherElsevier B.V.-
dc.relation.isformatofReproducció del document publicat a :
dc.relation.ispartofInsurance Mathematics and Economics, 2021, vol. 99, num. July, p. 1-8-
dc.rightscc-by-nc-nd (c) Elsevier B.V., 2021-
dc.subject.classificationAnàlisi de regressió-
dc.subject.classificationRisc (Assegurances)-
dc.subject.classificationTeoria de la predicció-
dc.subject.classificationAssegurances d'automòbils-
dc.subject.otherRegression analysis-
dc.subject.otherRisk (Insurance)-
dc.subject.otherPrediction theory-
dc.subject.otherAutomobile insurance-
dc.titleJoint generalized quantile and conditional tail expectation regression for insurance risk analysis-
Appears in Collections:Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)

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