.get_stan_summary

internal

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

.get_stan_summary function

<dl><dt>model_fit</dt>
<dd>stanfit object or list of stanfit objects.</dd>
<dt>params</dt>
<dd>Character vector. Names of params to pull from stan summary.</dd>
<dt>CrI</dt>
<dd>Numeric vector (length 2).</dd>
<dt>weights</dt>
<dd>Numeric vector. Weights for each model in model_fit, if list.</dd>
<dt>sampling_algorithm</dt>
<dd>Character vector for sampling method.</dd></dl>

Arguments

Author

Get stan summaries. — .get_stan_summary

<dl>

<dt>model_fit</dt>
<dd>stanfit object or list of stanfit objects.</dd>


<dt>params</dt>
<dd>Character vector. Names of params to pull from stan summary.</dd>


<dt>CrI</dt>
<dd>Numeric vector (length 2).</dd>


<dt>weights</dt>
<dd>Numeric vector. Weights for each model in model_fit, if list.</dd>


<dt>sampling_algorithm</dt>
<dd>Character vector for sampling method.</dd>

</dl>

.get_stan_summary: Get stan summaries.

Description

Usage

Value

Arguments

Author