[R-SIG-Finance] Question rmgarch package

[daniel melendez]

Hello All -  I am trying to complete a simulation based off of given information from a gogarchfit object but I seem to be obtaining really low VaR numbers.  Can anyone help explain this?  Also I saw on a previous post from the r-sig-finance mailing list that the standardized residuals can be obtain by taking the square root of the covariance matrix, however, I seem to be getting rather large numbers.  Can anyone help explain this too?  Any help would greatly appreciated.   The code snippets are below: # specs for GO-GARCH model
#----------------------------------------------------------------------------
spec = gogarchspec(mean.model = list(model = 'constant'),
  variance.model = list(model = 'eGARCH', 
  garchOrder = c(1, 1), variance.targeting = TRUE), 
  distribution = 'manig', ica = 'fastica', 
         ica.fix = list(A = est_mixing_matrix, K = whitening_matrix,
                        W = est_unmixing_matrix, U = est_rotation_matrix,
                        Kinv = dewhitening_matrix, Y = est_indep_components_matrix)) # GO-GARCH Fit
#----------------------------------------------------------------------------
cl <- makePSOCKcluster(3)
mod2 = gogarchfit(spec = spec, data = garchSeries, gfun = 'tanh', cluster = cl, 
                  solver = "hybrid", firstEig = 1, lastEig = 4, 
                  solver.control = list(trace = 1), 
                  maxiter1 = 40000, epsilon = 1e-08, rseed = 972,
   fit.control = list(stationarity = 1)) 
                                                        
stopCluster(cl)  # Simulation Generation
# --------------------------------------------------------------------------------
cl <- makePSOCKcluster(3)
sim1 <- gogarchsim(mod2, n.sim = 50, n.start = 0, 
   m.sim = 5000, 
   rseed = 214, cluster = cl)
stopCluster(cl) cf = convolution(sim1, weights = matrix(rep(1/10, 10), ncol = 10, nrow = 50)) 
VaR = matrix(NA, ncol = 2, nrow = 50)
colnames(VaR) = c('qfft[0.9999]', 'qfft[0.0001]')
for (i in 1:50){
    qfx = qfft(cf, index = i)
    VaR[i, 1] = qfx(0.9999)
    VaR[i, 2] = qfx(0.0001)
}
matplot(VaR, type = 'l')  #----------------------------------------------------------------------# Calculate the standardized residuals for the model
# ---------------------------------------------------------------------
residualsModel <- residuals(mod2)
cov.sq <- rcov(mod2)
sq.cov <- array(NA, dim = c(dim(cov.sq)[1],
                            dim(cov.sq)[2],
                            dim(cov.sq)[3]))
sq.cov.mat <- function (x){
    tmp = svd(x)
    sqrtx = tmp$u %*% sqrt(diag(tmp$d)) %*% t(tmp$u)
    return(sqrtx)
}
for(i in 1:dim(cov.sq)[3]){
    sq.cov[,,i] <- solve(sq.cov.mat(cov.sq[,,i]))
}
stand.resid <- matrix(NA, ncol = ncol(residualsModel), nrow = nrow(residualsModel))
for(i in 1:dim(sq.cov)[3]){
    stand.resid[i,] <- t(sq.cov[,,i] %*% as.numeric(residualsModel[i,]))  
}     Regards 
Daniel Melendez
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