Estimating Stochastic Volatility Models Using a Discrete Non-linear Filter. Working paper #3
AbstractMany approaches have been proposed for estimating stochastic volatility (SV) models, a number of which are filtering methods. While non-linear filtering methods are superior to linear approaches, non-linear filtering methods have not gained a wide acceptance in the econometrics literature due to their computational cost. This paper proposes a discretised non-linear filtering (DNF) algorithm for the estimation of latent variable models. It is shown that the DNF approach leads to significant computational gains relative to other procedures in the context of SV estimation without any associated loss in accuracy. It is also shown how a number of extensions to standard SV models can be accommodated within the DNF algorithm.
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Bibliographic InfoPaper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 3.
Date of creation: 15 Aug 2006
Date of revision:
non-linear filtering; stochastic volatility; state-space models; asymmetries; latent factors; two factor volatility models;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-01-28 (All new papers)
- NEP-ECM-2007-01-28 (Econometrics)
- NEP-ETS-2007-01-28 (Econometric Time Series)
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