Nonparametric Estimation in a Stochastic Volatility Model

  • In this paper we derive nonparametric stochastic volatility models in discrete time. These models generalize parametric autoregressive random variance models, which have been applied quite successfully to nancial time series. For the proposed models we investigate nonparametric kernel smoothers. It is seen that so-called nonparametric deconvolution estimators could be applied in this situation and that consistency results known for nonparametric errors- in-variables models carry over to the situation considered herein.

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Metadaten
Author:Jürgen Franke, Wolfgang Härdle, Jens-Peter Kreiss
URN (permanent link):urn:nbn:de:hbz:386-kluedo-10587
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (37)
Document Type:Preprint
Language of publication:English
Year of Completion:1998
Year of Publication:1998
Publishing Institute:Technische Universität Kaiserslautern
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:510 Mathematik

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