stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models

Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.

Version: 3.2.4
Depends: R (≥ 3.5)
Imports: Rcpp (≥ 1.0), coda (≥ 0.19), graphics, stats, utils, grDevices
LinkingTo: Rcpp, RcppArmadillo (≥ 0.9.900)
Suggests: testthat (≥ 2.3.2), mvtnorm, knitr
Published: 2024-03-03
DOI: 10.32614/CRAN.package.stochvol
Author: Darjus Hosszejni ORCID iD [aut, cre], Gregor Kastner ORCID iD [aut]
Maintainer: Darjus Hosszejni <darjus.hosszejni at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: stochvol citation info
Materials: NEWS
In views: Bayesian, Finance, TimeSeries
CRAN checks: stochvol results


Reference manual: stochvol.pdf
Vignettes: Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol


Package source: stochvol_3.2.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): stochvol_3.2.4.tgz, r-oldrel (arm64): stochvol_3.2.4.tgz, r-release (x86_64): stochvol_3.2.4.tgz, r-oldrel (x86_64): stochvol_3.2.4.tgz
Old sources: stochvol archive

Reverse dependencies:

Reverse imports: bayesianVARs, BGVAR, bsvars, factorstochvol, shrinkDSM, shrinkTVP
Reverse linking to: bayesianVARs, BGVAR, factorstochvol, shrinkDSM, shrinkTVP
Reverse suggests: bsreg, stochvolTMB, tensorBSS, tsBSS


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