rugarch package: is this forecast correct?
The calculation is numerically stable on my machine: I'd mark it up as computational/floating-point error and not worry about it. Michael On Mon, Dec 5, 2011 at 11:13 PM, Ivan Popivanov
<ivan.popivanov at gmail.com> wrote:
Let me start with the code:
library(quantmod)
library(rugarch)
getSymbols("SPY", from="1900-01-01")
rets=na.trim(diff(log(Cl(SPY))))
tt = tail(rets["/2004-10-29"], 1000)
spec = ugarchspec(variance.model=list(garchOrder=c(1,1)),
mean.model=list(armaOrder=c(2,5)), distribution.model="sged")
for(ii in 1:10)
{
? ttFit = ugarchfit( spec=spec, data=as.vector(tt), out.sample=0,
solver.control=list(trace=F) )
? ttFore = ugarchforecast( ttFit, n.ahead=1, n.roll=0 )
? print( as.array(ttFore)[,2,] )
}
Produces two different results: -0.001087313 and -0.001092084, each
repeated a few times.
What is the explanation for that? Since they are based on previous data, I
was expecting single step forecasts to produce the same result.
Thanks in advance!
Ivan
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