dcc_loglik

Obtains the log-likelihood of the cDCC model in the second step.
For details, see aielli2013dynamic;textualdccmidas and dcc_engle_2002;textualdccmidas.

internal

Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) <doi:10.1080/07350015.2013.771027>, the DCC-MIDAS of Colacito et al. (2011) <doi:10.1016/j.jeconom.2011.02.013>, the Asymmetric DCC of Cappiello et al. <doi:10.1093/jjfinec/nbl005>, and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) <doi:10.1080/07350015.2011.652048>. 'dccmidas' offers the possibility of including standard GARCH <doi:10.1016/0304-4076(86)90063-1>, GARCH-MIDAS <doi:10.1162/REST_a_00300> and Double Asymmetric GARCH-MIDAS <doi:10.1016/j.econmod.2018.07.025> models in the univariate estimation. Moreover, also the scalar and diagonal BEKK <doi:10.1017/S0266466600009063> models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications.

Vincenzo Candila

dccmidas

DCC Models with GARCH and GARCH-MIDAS Specifications in the
Univariate Step, RiskMetrics, Moving Covariance and Scalar and
Diagonal BEKK Models

dcc_loglik function

<dl><dt>param</dt>
<dd>Vector of starting values.</dd>
<dt>res</dt>
<dd>Array of standardized daily returns, coming from the first step estimation.</dd>
<dt>K_c</dt>
<dd>optional Number of initial observations to exclude from the estimation</dd></dl>

Arguments

cDCC log-likelihood (second step) — dcc_loglik

<dl>

<dt>param</dt>
<dd>Vector of starting values.</dd>


<dt>res</dt>
<dd>Array of standardized daily returns, coming from the first step estimation.</dd>


<dt>K_c</dt>
<dd>optional Number of initial observations to exclude from the estimation</dd>

</dl>

dcc_loglik: cDCC log-likelihood (second step)

Description

Usage

Value

Arguments

References