# grad_full_likelihood: Numerical gradient of the full log-likelihood function of the... In ccgarch: Conditional Correlation GARCH models

## Description

This function computes numerical gradient of the full log-likelihood function of the (E)DCC-GARCH(1,1) model with respect to its parameters.

## Usage

 1  grad.dcc.full(a, A, B, dcc.para, dvar, d=1e-5, model) 

## Arguments

 a a constant vector in the vector GARCH equation (N \times 1) A an ARCH parameter matrix in the vector GARCH equation (N \times N) B a GARCH parameter matrix in the vector GARCH equation (N \times N) dcc.para a vector of the DCC parameters (2 \times 1) dvar a matrix of the data used for estimating the (E)DCC-GARCH model (T \times N) d a step size for computing numerical gradient model a character string describing the model. "diagonal" for the diagonal model and "extended" for the extended (full ARCH and GARCH parameter matrices) model

## Value

a matrix of partial derivatives (T \times npar)

## Note

this function is currently not in use.

ccgarch documentation built on May 29, 2017, 12:58 p.m.