rmgarch (version 1.2-9)

goGARCHfit-class: class: GO-GARCH Fit Class

Description

Class for the GO-GARCH fitted object.

Objects from the Class

The class is returned by calling the function gogarchfit.

Extends

Class "mGARCHfit", directly. Class "GARCHfit", by class "mGARCHfit", distance 2. Class "rGARCH", by class "mGARCHfit", distance 3.

References

de Athayde, G.M. and Flores Jr, R.G. 2002, On Certain Geometric Aspects of Portfolio Optimisation with Higher Moments, mimeo. Broda, S.A. and Paolella, M.S. 2009, CHICAGO: A Fast and Accurate Method for Portfolio Risk Calculation, Journal of Financial Econometrics 7(4), 412--436 . Paolella, M.S. 2007, Intermediate Probability - A Computational Approach, Wiley-Interscience. Schmidt, R., Hrycej, T. and Stutzle 2006, Multivariate distribution models with generalized hyperbolic margins, Computational Statistics & Data Analysis 50(8), 2065-2096.

Examples

data(dji30ret)
spec = gogarchspec(mean.model = list(demean = "constant"), 
variance.model = list(model = "sGARCH", garchOrder = c(1,1), submodel = NULL), 
distribution.model = list(distribution = "manig"), ica = "fastica")
fit = gogarchfit(spec = spec, data  = dji30ret[,1:4, drop = FALSE],  
out.sample = 50, gfun = "tanh")
# The likelihood of the model
likelihood(fit)
# the GARCH coefficients of the independent factors
coef(fit)
# a news-impact surface plot
#ni = nisurface(fit, type = "cov", pair = c(1, 2), factor = c(1,2), plot = TRUE)
# the time varying correlation array
mc = rcor(fit)
# plot(mc[1,2,], type = "l")
# The moments of an equally weighted portfolio (subtract the out.sample from dimension)
gm = gportmoments(fit, weights = matrix(1/4, ncol = 4, 
nrow = dim(dji30ret)[1]-50), debug = TRUE)