print.model_weights

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.

Philippe Rast

bmgarch

Bayesian Multivariate GARCH Models

Stephen Martin

print.model_weights function

<dl><dt>x</dt>
<dd>Model weights object</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Author

Print method for model_weights — print.model_weights

<dl>

<dt>x</dt>
<dd>Model weights object</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

print.model_weights: Print method for model_weights

Description

Usage

Value

Arguments

Author