fracdiff.var(x, fracdiff.out, h)

## Generate a fractionally-differenced ARIMA(1,d,1) model :
ts.test <- fracdiff.sim(10000, ar = .2, ma = .4, d = .3)
## estimate the parameters in an ARIMA(1,d,1) model for the simulated series
fd.out <- fracdiff(ts.test$ser, nar= 1, nma = 1)

## Modify the covariance estimate by changing the finite-difference interval
(fd.o2 <- fracdiff.var(ts.test$series, fd.out, h = .0001))
## looks identical as  print(fd.out),
## however these (e.g.) differ :
vcov(fd.out)
vcov(fd.o2)

## A case, were the default variance is *clearly* way too small:
set.seed(1); fdc <- fracdiff(X <- fracdiff.sim(n=100,d=0.25)$series)
fdc
# Confidence intervals just based on asymp.normal approx. and std.errors:
confint(fdc) # ridiculously too narrow