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hal.structure.identifierCOLUMBIA UNIVERSITY USA
dc.contributor.authorChong, Carsten
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorHoffmann, Marc
hal.structure.identifierBaruch College [CUNY]
dc.contributor.authorLiu, Yanghui
hal.structure.identifierCentre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
dc.contributor.authorRosenbaum, Mathieu
hal.structure.identifierCentre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
dc.contributor.authorSzymanski, Grégoire
dc.date.accessioned2023-01-13T14:32:54Z
dc.date.available2023-01-13T14:32:54Z
dc.date.issued2022
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/23717
dc.language.isoenen
dc.subjectCentral limit theoremen
dc.subjectfractional Brownian motionen
dc.subjectHurst parameteren
dc.subjectnonparametric estimationen
dc.subjectrough volatilityen
dc.subjectspot volatilityen
dc.subjectvolatility of volatilityen
dc.subject.ddc519en
dc.titleStatistical inference for rough volatility: Central limit theoremsen
dc.typeDocument de travail / Working paper
dc.description.abstractenIn recent years, there has been substantive empirical evidence that stochastic volatility is rough. In other words, the local behavior of stochastic volatility is much more irregular than semimartingales and resembles that of a fractional Brownian motion with Hurst parameter H < 0.5. In this paper, we derive a consistent and asymptotically mixed normal estimator of H based on high-frequency price observations. In contrast to previous works, we work in a semiparametric setting and do not assume any a priori relationship between volatility estimators and true volatility. Furthermore, our estimator attains a rate of convergence that is known to be optimal in a minimax sense in parametric rough volatility models.en
dc.publisher.cityParisen
dc.identifier.citationpages47en
dc.relation.ispartofseriestitleCahier de recherche CEREMADE, Université Paris Dauphine-PSLen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.identifier.citationdate2022
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2023-01-13T14:26:34Z
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