bmgarch: Bayesian Multivariate GARCH Models

Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990) <doi:10.2307/2109358>. The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.

Version: 1.0.0
Depends: methods, R (≥ 3.5.0), Rcpp (≥ 1.0.1)
Imports: rstan (≥ 2.21.2), rstantools (≥ 1.5.1), ggplot2, MASS, forecast, loo, Rdpack
LinkingTo: BH (≥ 1.69.0-1), Rcpp (≥ 1.0.1), RcppEigen (≥, rstan (≥ 2.18.2), StanHeaders (≥ 2.18.1)
Suggests: testthat (≥ 2.1.0)
Published: 2020-09-17
Author: Philippe Rast ORCID iD [aut, cre], Stephen Martin ORCID iD [aut]
Maintainer: Philippe Rast < at>
License: GPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README
In views: Finance
CRAN checks: bmgarch results


Reference manual: bmgarch.pdf
Package source: bmgarch_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: not available, r-oldrel: bmgarch_1.0.0.tgz


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