gogarchspec-methods: function: GO-GARCH Specification

Description

Method for creating a GO-GARCH specification object prior to fitting.

Usage

gogarchspec(mean.model = list(model = c("constant", "AR", "VAR"), robust = FALSE, 
lag = 1, lag.max = NULL, lag.criterion = c("AIC", "HQ", "SC", "FPE"), 
external.regressors = NULL, 
robust.control = list("gamma" = 0.25, "delta" = 0.01, "nc" = 10, "ns" = 500)), 
variance.model = list(model = "sGARCH", garchOrder = c(1,1), submodel = NULL, 
variance.targeting = FALSE), distribution.model = c("mvnorm", "manig", "magh"), 
ica = c("fastica", "radical"), 
ica.fix = list(A = NULL, K = NULL), ...)

Arguments

mean.model

The mean specification. Allows for either a constant filtration of the return series, a univariate AR for each series with common lag (via the lag argument) else a classical or robust Vector Autoregressive Model (VAR). The r

variance.model

The univariate variance specification for the independent factors of the GO-GARCH model.

distribution.model

The distributions supported are the multivariate normal (mvnorm) and the multivariate affine NIG (manig) and GHYP (magh) distributions of Schmidt et al (see references).

ica

The algorithm to use for extracting the independent components. The fastica and radical algorithms are the only ICA algorithms currently allowed and local

ica.fix

This allows the option of supplying the mixing matrix (A) and optionally the whitening Matrix (K). This is likely to be use when comparing different models (with the same mean filtration and dataset but different variance models) and you wish to use

...

Value

A goGARCHspec object containing details of the GO-GARCH specification.