bayesDccGarch: Methods and Tools for Bayesian Dynamic Conditional Correlation GARCH(1,1) Model

Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014). <doi:10.1080/02664763.2013.839635>.

Version: 3.0.4
Depends: R (≥ 2.0), numDeriv, coda
Published: 2023-04-22
DOI: 10.32614/CRAN.package.bayesDccGarch
Author: Jose Augusto Fiorucci ORCID iD [aut, cre, cph], Ricardo Sanders Ehlers ORCID iD [aut, cph], Francisco Louzada ORCID iD [aut, cph]
Maintainer: Jose Augusto Fiorucci <jafiorucci at gmail.com>
BugReports: https://github.com/jafiorucci/bayesDccGarch/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://ui.adsabs.harvard.edu/abs/2014arXiv1412.2967F/abstract
NeedsCompilation: yes
Materials: README ChangeLog
In views: Bayesian
CRAN checks: bayesDccGarch results

Documentation:

Reference manual: bayesDccGarch.pdf

Downloads:

Package source: bayesDccGarch_3.0.4.tar.gz
Windows binaries: r-devel: bayesDccGarch_3.0.4.zip, r-release: bayesDccGarch_3.0.4.zip, r-oldrel: bayesDccGarch_3.0.4.zip
macOS binaries: r-release (arm64): bayesDccGarch_3.0.4.tgz, r-oldrel (arm64): bayesDccGarch_3.0.4.tgz, r-release (x86_64): bayesDccGarch_3.0.4.tgz, r-oldrel (x86_64): bayesDccGarch_3.0.4.tgz
Old sources: bayesDccGarch archive

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