factorstochvol

Gregor Kastner; Darjus Hosszejni

factorstochvol

Gregor Kastner^*
, Darjus Hosszejni

^*Corresponding author for this work

Institute for Statistics and Mathematics

Publication: Non-textual form › Software

Abstract

Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.

Original language	English
Place of Publication	Comprehensive R Archive Network (CRAN)
Publisher	The R Foundation
Media of output	Online
Publication status	Published - 9 Feb 2021

Austrian Classification of Fields of Science and Technology (ÖFOS)

102022 Software development
101018 Statistics
502025 Econometrics
101026 Time series analysis

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https://cran.r-project.org/package=factorstochvolLicence: GNU GPL

Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol
Hosszejni, D. & Kastner, G., 2021, In: Journal of Statistical Software. 100, 12, p. 1 - 34
Publication: Scientific journal › Journal article › peer-review

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title = "factorstochvol",

abstract = "Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving . Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix .",

author = "Gregor Kastner and Darjus Hosszejni",

year = "2021",

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day = "9",

language = "English",

publisher = "The R Foundation",

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TY - ADVS

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AU - Kastner, Gregor

AU - Hosszejni, Darjus

PY - 2021/2/9

Y1 - 2021/2/9

N2 - Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving . Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix .

AB - Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving . Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix .

M3 - Software

PB - The R Foundation

CY - Comprehensive R Archive Network (CRAN)

ER -

factorstochvol

Abstract

Austrian Classification of Fields of Science and Technology (ÖFOS)

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Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol

Cite this