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  "Title": "Bayesian Multivariate GARCH Models",
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  "Authors@R": "c(\nperson(given = \"Philippe\",\nfamily = \"Rast\",\nrole = c(\"aut\", \"cre\"),\nemail = \"rast.ph@gmail.com\",\ncomment = c(ORCID = \"0000-0003-3630-6629\")),\nperson(given = \"Stephen\",\nfamily = \"Martin\",\nrole = c(\"aut\"),\nemail = \"stephenSRMmartin@gmail.com\",\ncomment = c(ORCID = \"0000-0001-8085-2390\"))\n)",
  "Description": "Fit Bayesian multivariate GARCH models using 'Stan' for\nfull Bayesian inference. Generate (weighted) forecasts for\nmeans, variances (volatility) and correlations. Currently\nDCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q)\nparameterizations are implemented, alongside a constant\ncovariance baseline (that can be used for testing whether GARCH\nis warranted), based either on a multivariate gaussian normal\nor student-t distribution. DCC and CCC models are based on\nEngle (2002) <doi:10.1198/073500102288618487> and Bollerslev\n(1990). The BEKK parameterization follows Engle and Kroner\n(1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well\nas the estimation approach for this package is described in\nRast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted\nmodels contain 'rstan' objects and can be examined with 'rstan'\nfunctions.",
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