Package: stochvol 3.2.9

Darjus Hosszejni

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.

Authors:Darjus Hosszejni [aut, cre], Gregor Kastner [aut]

stochvol_3.2.9.tar.gz
stochvol_3.2.9.zip(r-4.7)stochvol_3.2.9.zip(r-4.6)stochvol_3.2.9.zip(r-4.5)
stochvol_3.2.9.tgz(r-4.6-x86_64)stochvol_3.2.9.tgz(r-4.6-arm64)stochvol_3.2.9.tgz(r-4.5-x86_64)stochvol_3.2.9.tgz(r-4.5-arm64)
stochvol_3.2.9.tar.gz(r-4.7-arm64)stochvol_3.2.9.tar.gz(r-4.7-x86_64)stochvol_3.2.9.tar.gz(r-4.6-arm64)stochvol_3.2.9.tar.gz(r-4.6-x86_64)
stochvol_3.2.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
stochvol/json (API)

# Install 'stochvol' in R:
install.packages('stochvol', repos = c('https://gregorkastner.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gregorkastner/stochvol/issues

Pkgdown/docs site:https://gregorkastner.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

9.51 score 18 stars 11 packages 141 scripts 1.9k downloads 48 exports 4 dependencies

Last updated from:b05b04bd3b. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK204
linux-devel-x86_64OK229
source / vignettesOK272
linux-release-arm64OK204
linux-release-x86_64OK239
macos-release-arm64OK202
macos-release-x86_64OK423
macos-oldrel-arm64OK276
macos-oldrel-x86_64OK534
windows-develOK188
windows-releaseOK231
windows-oldrelOK211
wasm-releaseOK167

Exports:default_fast_svget_default_fast_svget_default_general_svlatentlatent0logretparaparadensplotparatraceplotpredlatentpredvolapredypriorsruntimesampled_parametersspecify_priorssv_betasv_constantsv_exponentialsv_gammasv_infinitysv_inverse_gammasv_multinormalsv_normalsvbetasvlmsvlsamplesvlsample_rollsvsamplesvsample_fast_cppsvsample_general_cppsvsample_rollsvsample2svsimsvtausvtlsamplesvtlsample_rollsvtsamplesvtsample_rollthinningupdate_fast_svupdate_general_svupdate_regressorsupdate_t_errorupdatesummaryvalidate_and_process_expertvolavolplot

Dependencies:codalatticeRcppRcppArmadillo

Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
Introduction | Model specification and estimation | The stochvol package | Using stochvol within other samplers | Illustrative predictive exercise | Conclusion
Rendered fromarticle.Rnwusingknitr::knitr

Last update: 2026-03-06
Started: 2018-01-10

Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol
Introduction | Univariate SV models | Multivariate SV models | The stochvol package | The factorstochvol package | Summary and discussion
Rendered fromarticle2.Rnwusingknitr::knitr

Last update: 2026-03-06
Started: 2020-10-30

Readme and manuals

Help Manual

Help pageTopics
Efficient Bayesian Inference for Stochastic Volatility (SV) Modelsstochvol-package stochvol
Euro exchange rate dataexrates
Common Extractors for 'svdraws' and 'svpredict' Objectsextractors latent latent0 observations para predlatent predvola predy priors runtime sampled_parameters svbeta svtau thinning vola
Default Values for the Expert Settingsdefault_fast_sv get_default_fast_sv get_default_general_sv
Computes the Log Returns of a Time Serieslogret logret.default
Probability Density Function Plot for the Parameter Posteriorsparadensplot
Trace Plot of MCMC Draws from the Parameter Posteriorsparatraceplot
Trace Plot of MCMC Draws from the Parameter Posteriorsparatraceplot.svdraws
Graphical Summary of the Posterior Distributionplot.svdraws
Graphical Summary of the Posterior Predictive Distributionplot.svpredict
Prediction of Future Returns and Log-Volatilitiespredict.svdraws
Specify Prior Distributions for SV Modelsspecify_priors
Prior Distributions in 'stochvol'sv_beta sv_constant sv_exponential sv_gamma sv_infinity sv_inverse_gamma sv_multinormal sv_normal
Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility (SV) Modelsvlm
Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility (SV) Modelsvlsample svsample svsample2 svtlsample svtsample
Bindings to 'C++' Functions in 'stochvol'svsample_fast_cpp svsample_general_cpp
Rolling Estimation of Stochastic Volatility Modelssvlsample_roll svsample_roll svtlsample_roll svtsample_roll
Simulating a Stochastic Volatility Processsvsim
Single MCMC Update Using Fast SVupdate_fast_sv
Single MCMC Update Using General SVupdate_general_sv
Single MCMC update of Bayesian linear regressionupdate_regressors
Single MCMC update to Student's t-distributionupdate_t_error
Updating the Summary of MCMC Drawsupdatesummary
Validate and Process Argument 'expert'validate_and_process_expert
Plotting Quantiles of the Latent Volatilitiesvolplot