[R-SIG-Finance] Error with presigma in rugarch package

[Роман Хромотов]

Hi,
I'm new here, so please excuse me for violation of local rules, if this
happens. 

First, I've seen this problem here, it is more or less the same:
http://r.789695.n4.nabble.com/RUGARCH-bootstrap-fitting-error-presigma-length-td4634470.html <http://r.789695.n4.nabble.com/RUGARCH-bootstrap-fitting-error-presigma-length-td4634470.html> 
However, I have the newest version (I suppose so) 1.4-1 of rugarch package,
and the error is still in place. I think, there is a problem with my code
then. So, any help would be appreciated greatly. 

I'm trying to forecast some financial returns with so-called copula-garch
method. When I run the code woth ARMA(1,1) - it's ok, but with ARMA(2+, q)
it returns:
"Error in .sgarchpath2(spec = spec, n.sim = n.sim, n.start = n.start, m.sim
= m.sim,  : 
ugarchpath-->error: presigma must be of length 2"

Here is the simulation part of the code (originally taken from here with
minor changes of distribution model and a bit more:
http://www.unstarched.net/r-examples/rugarch/simulated-rolling-forecast-density/ <http://www.unstarched.net/r-examples/rugarch/simulated-rolling-forecast-density/>)


specm <- ugarchspec(variance.model=list(model="sGARCH", garchOrder=c(1,1),
submodel = NULL),
                   mean.model=list(armaOrder=c(2,1), include.mean=TRUE), 
distribution.model = 'std')
fsim <-  matrix(NA, ncol = 60, nrow = 5000)
# we will also create the closed form forecast
afor <-  matrix(NA, ncol = 60, nrow = 2)
rownames(afor) <-  c('Mu', 'Sigma')
colnames(afor) = colnames(fsim) = rep(paste('T+', 1:10, sep = ''), 6)
# T+1 we can use ugarchsim:
for (i in 0:5) {
 fit <-  ugarchfit(specm, returns[1:405, i+1])
 specf <- ugarchspec(variance.model=list(model="sGARCH", garchOrder=c(1,1),
submodel = NULL),
                     mean.model=list(armaOrder=c(2,1), include.mean=TRUE), 
distribution.model = 'std', fixed.pars = as.list(coef(fit)))
 tmp <-  ugarchsim(fit, n.start = 0, startMethod = 'sample', n.sim = 1,
m.sim = 5000, custom.dist = list(name = 'sample', distfit =
t(new_res[,i+1])))
 fsim[, (1+10*i)] = as.numeric(fitted(tmp))
 tmp <-  ugarchforecast(fit, n.ahead = 1)
 afor[, 1+10*i] <-  c(fitted(tmp), sigma(tmp))
}
### 
# for T+(i>1):
for (j in 0:5) {
 fit <-  ugarchfit(specm, returns[1:405, j+1])
 specf <- ugarchspec(variance.model=list(model="sGARCH", garchOrder=c(1,1),
submodel = NULL),
                     mean.model=list(armaOrder=c(2,1), include.mean=TRUE), 
distribution.model = 'std', fixed.pars = as.list(coef(fit)))
 for (i in 2:10) {
   filt <-  ugarchfilter(specf, data = returns[1:(405 + i - 1), j+1], n.old
= 405)
   path <-  ugarchpath(specf, n.sim = 1, m.sim = 5000, presigma =
tail(sigma(filt),
                                                                       1),
prereturns = returns[(405 + i - 1), j+1], preresiduals =
tail(residuals(filt),

1), custom.dist = list(name = 'sample', distfit = t(new_res[,j+1])))
   fsim[, i+10*j] <-  as.numeric(fitted(path))
   tmp <-  ugarchforecast(specf, returns[1:(405 + i - 1), j+1], n.ahead =
1)
   afor[, i+10*j] <- c(fitted(tmp), sigma(tmp)) 
 }
}

Hope, my point is clear! Thank you in advance!
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