Bayesian Multivariate GARCH Models
as.data.frame method for fitted.bmgarch objects.
as.data.frame method for forecast.bmgarch objects.
The 'bmgarch' package.
Estimate Bayesian Multivariate GARCH
Collect bmgarch objects into list.
Quantiles within lists
Internal function
Multiply matrices in array with a vector -- generic
Multiply matrices in array with a vector
Get stan summaries.
Print helper - Return new line(s).
Convert predictive array to data.frame.
Print helper for Sampling Config.
Print helper for BEKK/pdBEKK.
Print helper for beta component.
Print helper for CCC.
Print helper for DCC.
Print helper for LP component.
Print helper for means component.
Print helper for nu component.
Internal function to be used
Refit model
Print helper - Separator, new line
Simulate BEKK data.
Internal function to be used in sweep()
Print helper - tab
Fitted (backcasting) method for bmgarch objects.
Forecast method for bmgarch objects.
Leave-Future-Out Cross Validation (LFO-CV)
Model weights
Plot method for bmgarch objects.
Plot method for forecast.bmgarch objects.
Print method for fitted.bmgarch objects.
Print method for forecast.bmgarch objects.
print method for lfocv
Print method for model_weights
Print method for bmgarch.summary objects.
Standardize input data to facilitate computation
Summary method for bmgarch objects.
Models supported by bmgarch
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.