[R-SIG-Finance] RUGARCH eGARCH and variance targeting

[stoyan.stoyanov]

Thank you Alexios. I may have come across yet another but with the eGARCH
specification. It is NOT connected with variance targeting. I tried posting
it on the project's bug forum but it may have not gotten through. Just in
case:

I fit an eGARCH model to daily adjusted returns and bootstrap a forecast.
The forecast looks fine with n.ahead with values of 100 or higher but is
completely off with lower values of the forecast period (the difference
between a 99 and a 100-period-ahead forecast is striking). I can't see a
reason for that other than a bug (correct me if I'm wrong).

Here is the code:

spec=ugarchspec(variance.model=list(model="eGARCH", garchOrder=c(1,1),
                                    submodel=NULL, external.regressors=NULL,
variance.targeting=FALSE),
                mean.model=list(armaOrder=c(2,0), include.mean=TRUE,
archm=TRUE, 
                                archpow=1, arfima=FALSE,
external.regressors=NULL, archex=FALSE),
                distribution.model = "std", start.pars=list(),
fixed.pars=list())

fit=ugarchfit(spec, data, out.sample=100, solver="solnp",
solver.control=list(), 
                fit.control=list(stationarity=1, fixed.se=0, scale=0))

boot.pred=ugarchboot(fit, data=NULL, method="partial", n.ahead=99, 
                       n.bootfit=100, n.bootpred=500, out.sample=0,
rseed=NA, solver="solnp", 
                       solver.control=list(), fit.control=list(), 
                       external.forecasts=list(mregfor=NULL, vregfor=NULL),
parallel=TRUE, 
                       parallel.control=list(pkg="snowfall", cores=2))

Please let me know if you need a sample of the code or a copy of the
forecast.

Thanks,
Stoyan

-----
Stoyan Stoyanov
The University of Chicago Booth School of Business
MBA Class of 2013
(312) 532-0120 | stoyanov at chicagobooth.edu
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