tsmarch: Multivariate ARCH Models

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Version:	1.0.0
Depends:	R (≥ 3.5.0), methods, tsmethods (≥ 1.0.2)
Imports:	Rcpp, RcppParallel, tsgarch (≥ 1.0.3), tsdistributions (≥ 1.0.2), RcppBessel, Rsolnp, nloptr, numDeriv, abind, shape, Rdpack, xts, zoo, lubridate, sandwich, future.apply, future, stats, utils, data.table
LinkingTo:	Rcpp (≥ 0.10.6), RcppArmadillo, RcppParallel, RcppBessel
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), tstests
Published:	2024-11-18
DOI:	10.32614/CRAN.package.tsmarch
Author:	Alexios Galanos [aut, cre, cph]
Maintainer:	Alexios Galanos <alexios at 4dscape.com>
BugReports:	https://github.com/tsmodels/tsmarch/issues
License:	GPL-2
URL:	https://github.com/tsmodels/tsmarch, https://www.nopredict.com
NeedsCompilation:	yes
SystemRequirements:	GNU make
In views:	TimeSeries
CRAN checks:	tsmarch results

Documentation:

Reference manual:	tsmarch.pdf
Vignettes:	Feasible Multivariate GARCH Models (source, R code) tsmarch demo (source, R code)

Downloads:

Package source:	tsmarch_1.0.0.tar.gz
Windows binaries:	r-devel: tsmarch_1.0.0.zip, r-release: tsmarch_1.0.0.zip, r-oldrel: tsmarch_1.0.0.zip
macOS binaries:	r-release (arm64): tsmarch_1.0.0.tgz, r-oldrel (arm64): tsmarch_1.0.0.tgz, r-release (x86_64): tsmarch_1.0.0.tgz, r-oldrel (x86_64): tsmarch_1.0.0.tgz

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