bayesDccGarch: The Bayesian Dynamic Conditional Correlation GARCH 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: 2.2
Depends: R (≥ 2.0), numDeriv, coda
Published: 2021-04-07
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: ChangeLog
CRAN checks: bayesDccGarch results

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Reference manual: bayesDccGarch.pdf
Package source: bayesDccGarch_2.2.tar.gz
Windows binaries: r-devel: bayesDccGarch_2.2.zip, r-devel-UCRT: bayesDccGarch_2.2.zip, r-release: bayesDccGarch_2.2.zip, r-oldrel: bayesDccGarch_2.2.zip
macOS binaries: r-release (arm64): bayesDccGarch_2.2.tgz, r-release (x86_64): bayesDccGarch_2.2.tgz, r-oldrel: bayesDccGarch_2.2.tgz
Old sources: bayesDccGarch archive

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