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  • NON-iterative sampler REVISED

    Rights statement: This is the author’s version of a work that was accepted for publication in Statistics and Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics and Probability Letters, 126, 2017 DOI: 10.1016/j.spl.2017.02.035

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A non-iterative (trivial) method for posterior inference in stochastic volatility models

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A non-iterative (trivial) method for posterior inference in stochastic volatility models. / Tsionas, Mike G.
In: Statistics and Probability Letters, Vol. 126, 01.07.2017, p. 83-87.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, MG 2017, 'A non-iterative (trivial) method for posterior inference in stochastic volatility models', Statistics and Probability Letters, vol. 126, pp. 83-87. https://doi.org/10.1016/j.spl.2017.02.035

APA

Tsionas, M. G. (2017). A non-iterative (trivial) method for posterior inference in stochastic volatility models. Statistics and Probability Letters, 126, 83-87. https://doi.org/10.1016/j.spl.2017.02.035

Vancouver

Tsionas MG. A non-iterative (trivial) method for posterior inference in stochastic volatility models. Statistics and Probability Letters. 2017 Jul 1;126:83-87. Epub 2017 Mar 6. doi: 10.1016/j.spl.2017.02.035

Author

Tsionas, Mike G. / A non-iterative (trivial) method for posterior inference in stochastic volatility models. In: Statistics and Probability Letters. 2017 ; Vol. 126. pp. 83-87.

Bibtex

@article{46a38388fdf8429ea71a9eb65f4c1c0a,
title = "A non-iterative (trivial) method for posterior inference in stochastic volatility models",
abstract = "We propose a new non-iterative, very simple but accurate, Bayesian inference procedure for the stochastic volatility model. The only requirement of our approach is to solve a large, sparse linear system which we avoid by iteration.",
keywords = "Stochastic volatility model, Monte Carlo methods, Markov Chain Monte Carlo, Iterative methods",
author = "Tsionas, {Mike G.}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Statistics and Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics and Probability Letters, 126, 2017 DOI: 10.1016/j.spl.2017.02.035",
year = "2017",
month = jul,
day = "1",
doi = "10.1016/j.spl.2017.02.035",
language = "English",
volume = "126",
pages = "83--87",
journal = "Statistics and Probability Letters",
issn = "0167-7152",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - A non-iterative (trivial) method for posterior inference in stochastic volatility models

AU - Tsionas, Mike G.

N1 - This is the author’s version of a work that was accepted for publication in Statistics and Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics and Probability Letters, 126, 2017 DOI: 10.1016/j.spl.2017.02.035

PY - 2017/7/1

Y1 - 2017/7/1

N2 - We propose a new non-iterative, very simple but accurate, Bayesian inference procedure for the stochastic volatility model. The only requirement of our approach is to solve a large, sparse linear system which we avoid by iteration.

AB - We propose a new non-iterative, very simple but accurate, Bayesian inference procedure for the stochastic volatility model. The only requirement of our approach is to solve a large, sparse linear system which we avoid by iteration.

KW - Stochastic volatility model

KW - Monte Carlo methods

KW - Markov Chain Monte Carlo

KW - Iterative methods

U2 - 10.1016/j.spl.2017.02.035

DO - 10.1016/j.spl.2017.02.035

M3 - Journal article

VL - 126

SP - 83

EP - 87

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

SN - 0167-7152

ER -