dcc_gradient

param

matrix of conditional variance vectors (T by N)

matrix of residual(de-mean) returns (T by N)

residuals

matrix of standrdized(De-GARCH) residual returns (T by N)

stdresids

unconditional correlation matrix of stdresids (N by N)

uncR

This functions calculates numerical gradient of log-likelihood of DCC-GARCH model.

Functions for Estimating a (c)DCC-GARCH Model in large dimensions
based on a publication by Engle et,al (2017) <doi:10.1080/07350015.2017.1345683> and Nakagawa et,al (2018) <doi:10.3390/ijfs6020052>.
This estimation method is consist of composite likelihood method by Pakel et al. (2014) <http://paneldataconference2015.ceu.hu/Program/Cavit-Pakel.pdf>
and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (<doi:10.1016/S0047-259X(03)00096-4>,
<doi:10.1214/12-AOS989>, <doi:10.1016/j.jmva.2015.04.006>).

dcc_gradient: This functions calculates numerical gradient of log-likelihood of DCC-GARCH model.

Description

Usage

Arguments

Value