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). 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.
Version: 2.0.0 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: testthat (≥ 2.3.2) Published: 2023-09-12 DOI: 10.32614/CRAN.package.bmgarch Author: Philippe Rast [aut, cre], Stephen Martin [aut] Maintainer: Philippe Rast <rast.ph at gmail.com> BugReports: https://github.com/ph-rast/bmgarch/issues License: GPL (≥ 3) NeedsCompilation: yes SystemRequirements: GNU make Materials: README NEWS In views: Finance CRAN checks: bmgarch results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bmgarch to link to this page.
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