.sim.bekk

Simulates time series data from specified BEKK model.

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

.sim.bekk function

<dl><dt>N</dt>
<dd>Integer. Length of time series.</dd>
<dt>C</dt>
<dd>Numeric square matrix. Constant covariance matrix (C). Must be symmetric.</dd>
<dt>A</dt>
<dd>Numeric square matrix. Moving average GARCH matrix (A).</dd>
<dt>B</dt>
<dd>Numeric square matrix. Autoregressive ARCH matrix (B).</dd>
<dt>phi</dt>
<dd>Numeric square matrix (Optional). Autoregressive coefficients (Phi).</dd>
<dt>theta</dt>
<dd>Numeric square matrix (Optional). Moving average coefficients (Theta).</dd></dl>

Arguments

Author

Simulate BEKK data. — .sim.bekk

<dl>

<dt>N</dt>
<dd>Integer. Length of time series.</dd>


<dt>C</dt>
<dd>Numeric square matrix. Constant covariance matrix (C). Must be symmetric.</dd>


<dt>A</dt>
<dd>Numeric square matrix. Moving average GARCH matrix (A).</dd>


<dt>B</dt>
<dd>Numeric square matrix. Autoregressive ARCH matrix (B).</dd>


<dt>phi</dt>
<dd>Numeric square matrix (Optional). Autoregressive coefficients (Phi).</dd>


<dt>theta</dt>
<dd>Numeric square matrix (Optional). Moving average coefficients (Theta).</dd>

</dl>

.sim.bekk: Simulate BEKK data.

Description

Usage

Value

Arguments

Author

Details