A streamlined simulation function to simulate from FIGJR-GARCH models.
figjrgarch_sim(
pars = list(mu = 0, ar = numeric(0), ma = numeric(0), D = 0, omega = 4e-04, phi = 0.2,
beta = 0.4, gamma = 0.1, d = 0.25, df = 10, shape = 2, P = 3, skew = 1),
cond_dist = c("norm", "std", "ged", "ald", "snorm", "sstd", "sged", "sald"),
n = 1000,
nstart = 5000,
trunc = "none"
)A list with four elements is returned: rt are the simulated
observations, etat are the underlying innovations,
sigt are the correspondingly simulated conditional
standard deviations, and cmeans are the simulated
conditional means. These four elements are formatted as
"ts" class time series objects.
a named list with the parameter specifications; the user can provide a named list with only the settings they would like to adjust relative to the default settings.
a one-element character vector specifying the conditional distribution to consider.
the number of observations to return.
the number of burn-in observations to simulate before
the final n values to keep; the first nstart values
are not returned; if a dual model, i.e. with model in the conditional
mean and in the conditional variance, is considered, two times nstart
is considered in the first simulation step in the conditional variance,
so that n + nstart values can be fed into the second simulation
step for the conditional mean.
a truncation for the finite-order coefficient series
in long-memory models; can either be the character "none" for truncation
back to the very first observation at each time point, or to any positive integer
for setting the corresponding truncation length of the infinite-order representation
polynomial.
See the documentation on figjrgarch for information
on the FIGJR-GARCH model. This function provides
an easy way to simulate from these models.
sim <- figjrgarch_sim(n = 1000)
mat <- do.call(cbind, sim)
plot(mat, main = "")
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