summary.bmgarch

Computes posterior summaries for all parameters of interest for bmgarch objects.

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

summary.bmgarch function

<dl><dt>object</dt>
<dd>bmgarch object.</dd>
<dt>CrI</dt>
<dd>Numeric vector (Default: <code>c(.025, .975)</code>). Lower and upper bound of predictive credible interval.</dd>
<dt>digits</dt>
<dd>Integer (Default: 2, optional). Number of digits to round to when printing.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Author

Summary method for bmgarch objects. — summary.bmgarch

<dl>

<dt>object</dt>
<dd>bmgarch object.</dd>


<dt>CrI</dt>
<dd>Numeric vector (Default: <code>c(.025, .975)</code>). Lower and upper bound of predictive credible interval.</dd>


<dt>digits</dt>
<dd>Integer (Default: 2, optional). Number of digits to round to when printing.</dd>


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

</dl>

summary.bmgarch: Summary method for bmgarch objects.

Description

Usage

Value

Arguments

Author