Nonlinear smoother for stochastic volatility model

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F01%3A00064629" target="_blank" >RIV/49777513:23520/01:00064629 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Nonlinear smoother for stochastic volatility model
Original language description
A new technique for nonlinear state and parameter estimation of the discrete time stochastic volatility models in the state space form is developed. The Gibbs sampler is used to construct a Markov-chain simulation tool that reflects both inherent model variability and parameter uncertainty. The Gibbs sampling algorithm is derived from the generalized data-augmentation method and the iterative Monte Carlo simulation procedures to calculating marginal state and parameters probability density functions. The design algorithm generates a loop where samples from the correspondent data augmented probability density function are drawn. The proposed chain converges to equilibrium enabling to summarize the unobserved variance states and unknown model parametersdistributions. The non-Gaussian density of the log of squared inovations is advantageously modelled as a mixture of Gaussians.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AH - Economics
OECD FORD branch
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Project
<a href="/en/project/GA102%2F01%2F0021" target="_blank" >GA102/01/0021: Nonlinear estimation and change detection for stochastic systems</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2001
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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