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factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

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>.

Version: 1.1.0
Depends: R (≥ 3.0.2)
Imports: GIGrvg (≥ 0.4), Rcpp (≥ 1.0.0), corrplot, methods, grDevices, graphics, stats, utils, stochvol (≥ 3.0.2)
LinkingTo: Rcpp, RcppArmadillo (≥ 0.9.900), stochvol
Suggests: LSD (≥ 4.0-0), coda (≥ 0.19-2), knitr, RColorBrewer, testthat (≥ 2.1.0), zoo
Published: 2023-11-24
DOI: 10.32614/CRAN.package.factorstochvol
Author: Gregor Kastner ORCID iD [aut, cre], Darjus Hosszejni ORCID iD [ctb], Luis Gruber ORCID iD [ctb]
Maintainer: Gregor Kastner <gregor.kastner at aau.at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: factorstochvol citation info
Materials: NEWS
In views: Bayesian, Finance, TimeSeries
CRAN checks: factorstochvol results

Documentation:

Reference manual: factorstochvol.pdf
Vignettes: Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol

Downloads:

Package source: factorstochvol_1.1.0.tar.gz
Windows binaries: r-devel: factorstochvol_1.1.0.zip, r-release: factorstochvol_1.1.0.zip, r-oldrel: factorstochvol_1.1.0.zip
macOS binaries: r-release (arm64): factorstochvol_1.1.0.tgz, r-oldrel (arm64): factorstochvol_1.1.0.tgz, r-release (x86_64): factorstochvol_1.1.0.tgz, r-oldrel (x86_64): factorstochvol_1.1.0.tgz
Old sources: factorstochvol archive

Reverse dependencies:

Reverse imports: bayesianVARs
Reverse linking to: bayesianVARs

Linking:

Please use the canonical form https://CRAN.R-project.org/package=factorstochvol to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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