dccmidas_mat_est

Obtains the matrix H_t, R_t and long-run correlations, under the DCC-MIDAS model
For details, see colacito2011component;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

dccmidas_mat_est function

<dl><dt>est_param</dt>
<dd>Vector of estimated values</dd>
<dt>res</dt>
<dd>Array of standardized daily returns, coming from the first step estimation</dd>
<dt>Dt</dt>
<dd>Matrix of conditional standard deviations (coming from the first step)</dd>
<dt>lag_fun</dt>
<dd>optional. Lag function to use. Valid choices are "Beta" (by default) and "Almon",
for the Beta and Exponential Almon lag functions, respectively</dd>
<dt>N_c</dt>
<dd>Number of (lagged) realizations to use for the standarized residuals forming the long-run correlation</dd>
<dt>K_c</dt>
<dd>Number of (lagged) realizations to use for the long-run correlation</dd></dl>

Arguments

Obtains the matrix H_t, R_t and long-run correlations, under the DCC-MIDAS model — dccmidas_mat_est

<dl>

<dt>est_param</dt>
<dd>Vector of estimated values</dd>


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


<dt>Dt</dt>
<dd>Matrix of conditional standard deviations (coming from the first step)</dd>


<dt>lag_fun</dt>
<dd>optional. Lag function to use. Valid choices are "Beta" (by default) and "Almon",
for the Beta and Exponential Almon lag functions, respectively</dd>


<dt>N_c</dt>
<dd>Number of (lagged) realizations to use for the standarized residuals forming the long-run correlation</dd>


<dt>K_c</dt>
<dd>Number of (lagged) realizations to use for the long-run correlation</dd>

</dl>

dccmidas_mat_est: Obtains the matrix H_t, R_t and long-run correlations, under the DCC-MIDAS model

Description

Usage

Value

Arguments

References