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.

Version: 1.3.0
Depends: R (≥ 3.0.2), coda
Imports: Rcpp (≥ 0.11), methods, stats, graphics, utils
LinkingTo: Rcpp, RcppArmadillo (≥ 0.4)
Suggests: mvtnorm
Published: 2016-08-24
Author: Gregor Kastner [aut, cre]
Maintainer: Gregor Kastner <gregor.kastner 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_1.3.0.tar.gz
Windows binaries: r-devel: stochvol_1.3.0.zip, r-release: stochvol_1.3.0.zip, r-oldrel: stochvol_1.3.0.zip
OS X Mavericks binaries: r-release: stochvol_1.3.0.tgz, r-oldrel: stochvol_1.3.0.tgz
Old sources: stochvol archive

Reverse dependencies:

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

Linking:

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