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.
Author: | Jürgen Franke, Wolfgang Härdle, Jens-Peter Kreiss |
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URN: | urn:nbn:de:hbz:386-kluedo-10587 |
Series (Serial Number): | Report in Wirtschaftsmathematik (WIMA Report) (37) |
Document Type: | Preprint |
Language of publication: | English |
Year of Completion: | 1998 |
Year of first Publication: | 1998 |
Publishing Institution: | Technische Universität Kaiserslautern |
Date of the Publication (Server): | 2000/08/28 |
Faculties / Organisational entities: | Kaiserslautern - Fachbereich Mathematik |
DDC-Cassification: | 5 Naturwissenschaften und Mathematik / 510 Mathematik |
Licence (German): | Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011 |