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) and Hosszejni and Kastner (2019) ; the most common use cases are described in Hosszejni and Kastner (2021) and Kastner (2016) and the package examples.


Reference manual

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install.packages("stochvol")

3.2.9 by Darjus Hosszejni, 7 months ago


https://gregorkastner.github.io/stochvol/


Report a bug at https://github.com/gregorkastner/stochvol/issues


Browse source code at https://github.com/cran/stochvol


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


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp, coda, graphics, stats, utils, grDevices

Suggests testthat, mvtnorm, knitr

Linking to Rcpp, RcppArmadillo


Imported by BGVAR, bayesianVARs, bsvars, dsp, factorstochvol, shrinkDSM, shrinkTVP, shrinkTVPVAR.

Suggested by DynCount, bsreg, stochvolTMB, tensorBSS, tsBSS.


See at CRAN