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

Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Fr├╝hwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002>; the most common use cases are described in Kastner (2016) <doi:10.18637/jss.v069.i05>. Also incorporates SV with leverage.

Version: 2.0.3
Depends: R (≥ 3.0.2), coda
Imports: Rcpp (≥ 0.11), graphics, stats, utils
LinkingTo: Rcpp, RcppArmadillo (≥ 0.4)
Suggests: mvtnorm
Published: 2019-05-29
Author: Gregor Kastner ORCID iD [aut], Darjus Hosszejni ORCID iD [aut, cre]
Maintainer: Darjus Hosszejni <darjus.hosszejni at wu.ac.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

Downloads:

Reference manual: stochvol.pdf
Vignettes: Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
Heavy-Tailed Innovations in the R Package stochvol
Package source: stochvol_2.0.3.tar.gz
Windows binaries: r-devel: stochvol_2.0.3.zip, r-release: stochvol_2.0.3.zip, r-oldrel: stochvol_2.0.3.zip
OS X binaries: r-release: stochvol_2.0.3.tgz, r-oldrel: stochvol_2.0.3.tgz
Old sources: stochvol archive

Reverse dependencies:

Reverse imports: factorstochvol
Reverse linking to: factorstochvol
Reverse suggests: tensorBSS, tsBSS

Linking:

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