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) and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) . The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.


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1.1.0 by Philippe Rast, a month ago

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Authors: Philippe Rast [aut, cre] , Stephen Martin [aut]

Documentation:   PDF Manual  

Task views: Empirical Finance

GPL (>= 3) license

Imports forecast, ggplot2, loo, MASS, Rdpack, rstan, rstantools

Depends on methods, Rcpp

Suggests testthat

Linking to BH, Rcpp, RcppParallel, RcppEigen, RcppParallel.1, rstan, StanHeaders

System requirements: GNU make

See at CRAN