Nonparametric modelling of the conditional distribution in a stochastic volatility model
AbstractStochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscript, we discuss a penalized maximum likelihood approach for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1308.5836.
Date of creation: Aug 2013
Date of revision: Sep 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-08-31 (All new papers)
- NEP-ECM-2013-08-31 (Econometrics)
- NEP-ETS-2013-08-31 (Econometric Time Series)
- NEP-FOR-2013-08-31 (Forecasting)
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