grad_full_likelihood: Numerical gradient of the full log-likelihood function of the...

In hoanguc3m/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

    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.

hoanguc3m/ccgarch documentation built on May 29, 2019, 11:05 p.m.