gogarchspec-methods: function: GO-GARCH Specification
In rgarch: Flexible GARCH modelling in R

Description Usage Arguments Value Author(s)

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

gogarchspec(mean.model = list(demean = c("constant", "AR", "VAR", "robVAR"), 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 = list(distribution = c("mvnorm", "manig", "magh")), 
ica = c("fastica", "pearson", "jade", "radical"), ica.fix = list(A = NULL, K = NULL), ...)

`mean.model`	The mean specification. Allows for either a constant demeaning of the return series or a univariate AR for each series with common lag (via the “lag” argument) else a classical or robust Vector Autoregressive Model (VAR). The ‘robVAR’ option allows for a robust version of VAR based on the multivariate Least Trimmed Squares Estimator described in Croux and Joossens (2008). The ‘robust.control’ includes additional tuning parameters to the robust regression including the proportion to trim (“gamma”), the critical value for Reweighted estimator (“delta”), the number of subsets (“ns”) and the number of C-steps (“nc”). The external.regressors argument allows for a matrix of external regressors in the VAR formulation, and can also be used as a common external regressors in the constant or AR specifications.
`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 described in the “africa” package in R-Forge, the “jade” is the algorithm by J.F. Cardoso and described in the “JADE” package and the “pearson” is a mutual information based algorithm described in the “PearsonICA” package.
`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 and you wish to use the same independent factors.
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