Nonparametric estimation for a stochastic volatility model

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dc.creatorComte, Fabienne
dc.creatorGenon-Catalot, Valentine
dc.creatorRozenholc, Yves
dc.date2007-12-21
dc.date.accessioned2026-07-07T08:50:58Z
dc.date.available2026-07-07T08:50:58Z
dc.descriptionConsider discrete time observations (X_{\ellδ})_{1\leq \ell \leq n+1}$ of the process $X$ satisfying $dX_t= \sqrt{V_t} dB_t$, with $V_t$ a one-dimensional positive diffusion process independent of the Brownian motion $B$. For both the drift and the diffusion coefficient of the unobserved diffusion $V$, we propose nonparametric least square estimators, and provide bounds for theirrisk. Estimators are chosen among a collection of functions belonging to a finite dimensional space whose dimension is selected by a data driven procedure. Implementation on simulated data illustrates how the method works.
dc.identifierhttps://arxiv.org/abs/0712.3735
dc.identifierhttp://arxiv.org/abs/0712.3735
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/144781
dc.subjectMethodology
dc.subjectStatistics Theory
dc.titleNonparametric estimation for a stochastic volatility model
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