Optimal stochastic volatility models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F01%3A00064868" target="_blank" >RIV/49777513:23520/01:00064868 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Optimal stochastic volatility models
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 is based on 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 parameters distributions. 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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Classification
Type
V<sub>x</sub> - Unclassified - Research report containing classified information
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
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)
Others
Publication year
2001
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Number of pages
1
Place of publication
Plzeň
Publisher/client name
Západočeská univerzita
Version
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