Bayesian Multivariate GARCH Models R package bmgarch version 2.1.0 CRAN Task View: Finance (https://CRAN.R-project.org/view=Finance) 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, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), 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). 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/osf.io/j57pk>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions. Software Depends: methods, R (>= 4.0.0), Rcpp (>= 1.0.5) Imports: forecast, ggplot2, loo, MASS, Rdpack, rstan (>= 2.26.0), rstantools (>= 2.1.1) LinkingTo: BH (>= 1.72.0-0), Rcpp (>= 1.0.5), RcppParallel (>= 5.0.1), RcppEigen (>= 0.3.3.7.0), RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0) Suggests: cmdstanr, posterior, testthat (>= 2.3.2) Philippe Rast <rast.ph@gmail.com> Comprehensive R Archive Network (CRAN) Philippe Rast [aut, cre] (ORCID: <https://orcid.org/0000-0003-3630-6629>), Stephen Martin [aut] (ORCID: <https://orcid.org/0000-0001-8085-2390>) GPL (>= 3) 2026-05-21 application/tgz https://CRAN.R-project.org/package=bmgarch doi:10.32614/CRAN.package.bmgarch