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stochvol  

Efficient Bayesian Inference for Stochastic Volatility (SV) Models
View on CRAN: Click here


Download and install stochvol package within the R console
Install from CRAN:
install.packages("stochvol")

Install from Github:
library("remotes")
install_github("cran/stochvol")

Install by package version:
library("remotes")
install_version("stochvol", "3.2.5")



Attach the package and use:
library("stochvol")
Maintained by
Darjus Hosszejni
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-01-23
Latest Update: 2024-03-03
Description:
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.
How to cite:
Darjus Hosszejni (2013). stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models. R package version 3.2.5, https://cran.r-project.org/web/packages/stochvol. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.5-0 (2013-01-23 16:29), 0.5-1 (2013-01-28 15:46), 0.6-0 (2013-04-17 19:22), 0.6-1 (2013-07-09 14:57), 0.7-0 (2013-10-10 00:26), 0.7-1 (2013-10-22 00:57), 0.8-0 (2014-02-06 20:02), 0.8-1 (2014-02-07 00:32), 0.8-2 (2014-05-16 07:39), 0.8-4 (2014-07-01 14:52), 0.9-0 (2014-10-16 23:02), 0.9-1 (2014-12-27 23:38), 1.0.0 (2015-01-28 16:00), 1.1.0 (2015-05-15 22:02), 1.1.1 (2015-05-23 02:03), 1.1.2 (2015-06-29 19:25), 1.1.3 (2015-06-30 08:42), 1.2.0 (2015-07-15 23:48), 1.2.1 (2015-11-27 08:53), 1.2.2 (2016-01-01 22:14), 1.2.3 (2016-02-18 08:56), 1.3.0 (2016-08-24 22:08), 1.3.1 (2016-10-19 00:39), 1.3.2 (2016-10-26 14:28), 1.3.3 (2017-09-19 02:32), 2.0.0 (2019-02-01 09:40), 2.0.1 (2019-02-26 23:10), 2.0.2 (2019-03-27 21:20), 2.0.3 (2019-05-29 07:30), 2.0.4 (2019-06-26 12:30), 3.0.0 (2020-11-03 08:10), 3.0.1 (2020-11-04 17:30), 3.0.2 (2020-11-17 18:50), 3.0.3 (2020-11-24 16:30), 3.0.4 (2021-02-09 20:20), 3.0.5 (2021-05-16 16:20), 3.0.6 (2021-05-20 17:20), 3.1.0 (2021-07-12 17:20), 3.2.0 (2021-11-26 22:30), 3.2.1 (2023-03-10 13:50), 3.2.3 (2023-11-27 00:30), 3.2.4 (2024-03-03 13:10)
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