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dc.contributor.authorAretz, Kevin
dc.contributor.authorArisoy, Eser
dc.date.accessioned2017-03-14T14:33:25Z
dc.date.available2017-03-14T14:33:25Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16340
dc.descriptionRevise and Resubmit. Journal of Financial and Quantitative Analysis.
dc.language.isoenen
dc.subjectAsset pricing, ex-ante physical skew, realized skewness, quantile regression models
dc.subjectG11
dc.subjectG12
dc.subjectG15
dc.subject.classificationjelG.G1.G15en
dc.subject.classificationjelG.G1.G12en
dc.subject.classificationjelG.G1.G11en
dc.titleDo Stock Markets Price Skewness? New Evidence from Quantile Regression Skewness Forecasts
dc.typeDocument de travail / Working paper
dc.contributor.editoruniversityotherManchester Business School
dc.description.abstractenWe use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an asset's future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neuberger's (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.
dc.publisher.nameUniversité Paris-Dauphine
dc.publisher.cityParis
dc.relation.ispartofseriestitleCahier de recherche DRM
dc.identifier.urlsitehttp://dx.doi.org/10.2139/ssrn.2494291
dc.contributor.countryeditoruniversityotherUNITED KINGDOM
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-04-19T12:51:51Z
hal.person.labIds164073
hal.person.labIds1032


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