DCCGARCHselection

This function calculates the optimal DCC-GARCH specification

The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.

David Gabauer

ConnectednessApproach

Connectedness Approach

DCCGARCHselection function

<dl><dt>x</dt>
<dd>zoo data matrix</dd>
<dt>distributions</dt>
<dd>Vector of distributions</dd>
<dt>models</dt>
<dd>Vector of GARCH models</dd>
<dt>prob</dt>
<dd>The quantile (coverage) used for the VaR.</dd>
<dt>conf.level</dt>
<dd>Confidence level of VaR test statistics</dd>
<dt>lag</dt>
<dd>Lag length of weighted Portmanteau statistics</dd>
<dt>ar</dt>
<dd>AR(p)</dd>
<dt>ma</dt>
<dd>MA(q)</dd></dl>

Arguments

Author

DCC-GARCH selection specification — DCCGARCHselection

<dl>

<dt>x</dt>
<dd>zoo data matrix</dd>


<dt>distributions</dt>
<dd>Vector of distributions</dd>


<dt>models</dt>
<dd>Vector of GARCH models</dd>


<dt>prob</dt>
<dd>The quantile (coverage) used for the VaR.</dd>


<dt>conf.level</dt>
<dd>Confidence level of VaR test statistics</dd>


<dt>lag</dt>
<dd>Lag length of weighted Portmanteau statistics</dd>


<dt>ar</dt>
<dd>AR(p)</dd>


<dt>ma</dt>
<dd>MA(q)</dd>

</dl>

DCCGARCHselection: DCC-GARCH selection specification

Description

Usage

Value

Arguments

Author

References