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I am a beginner in R and my econometrics background is not very sound either. I want to build a constant conditional correlation GARCH (1,1) model in R and I found the function, the description of which I have copy-pasted below. This functions requires that you calculate the individual matrices and vectors individually and then you plug them into the function. The problem is that I do not know how to do it individually. Is there any package that calculates the GARCH coefficients automatically?

Thanks a lot!

Simulating an (E)CCC-GARCH(1,1) process

Description

This function simulates data either from the original CCC-GARCH by Bollerslev (1990) or from the Extended CCC-GARCH that has non-zero off-diagonal entries in the parameter matrices in the GARCH equation. The innovations (the standardised residuals) can be either a normal or student's $t$ distribution.

The dimension (N) is determined by the number of elements in the \mathbf{a} vector.

Usage

eccc.sim(nobs, a, A, B, R, d.f=Inf, cut=1000, model) Arguments

nobs
a number of observations to be simulated (T)

a
a vector of constants in the GARCH equation (N \times 1)

A
an ARCH parameter matrix in the GARCH equation. \mathbf{A} can be a diagonal matrix for the original CCC-GARCH model or a full matrix for the extended model (N \times N)

B
a GARCH parameter matrix in the GARCH equation. \mathbf{B} can be a diagonal matrix for the original CCC-GARCH model or a full matrix for the extended model (N \times N)

R
a constant conditional correlation matrix (N \times N)

d.f the degrees of freedom parameter for the t-distribution

cut the number of observations to be thrown away for removing initial effects of simulation

model
a character string describing the model. "diagonal" for the diagonal model and "extended" for the extended (full ARCH and GARCH parameter matrices) model

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    $\begingroup$ Hi @Efi and welcome to quant.SE! I did not understand your question: do you need only an R package that estimates a GARCH(p,q) model? $\endgroup$ – Quantopik May 4 '15 at 13:30

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