uGARCHdistribution-class {rugarch} | R Documentation |
class: Univariate GARCH Parameter Distribution Class
Description
Class for the univariate GARCH Parameter Distribution.
Objects from the Class
A virtual Class: No objects may be created from it.
Extends
Class "GARCHdistribution"
, directly.
Class "rGARCH"
, by class "GARCHdistribution", distance 2.
Methods
- as.data.frame
signature(x = "uGARCHdistribution")
: Extracts various values from object (see note).- plot
signature(x = "uGARCHdistribution", y = "missing")
: Parameter Distribution Plots.- show
signature(object = "uGARCHdistribution")
: Parameter Distribution Summary.
Note
The as.data.frame
function takes optionally 2 additional arguments,
namely window
which indicates the particular distribution window number
for which data is required (is usually just 1 unless the recursive option was
used), and which
indicating the type of data required. Valid values for
the latter are “rmse” for the root mean squared error between simulation
fit and actual parameters, “stats” for various statistics computed for
the simulations such as log likelihood, persistence, unconditional variance and
mean, “coef” for the estimated coefficients (i.e. the parameter
distribution and is the default choice), and “coefse” for the estimated
robust standard errors of the coefficients (i.e. the parameter standard error
distribution).
The plot method offers 4 plot types, namely, Parameter Density Plots (take
window
as additional argument), Bivariate Plots (take window
as
additional argument), Stats and RMSE (only when recursive option used) Plots.
The standard option for which
is used, allowing for a numeric arguments
to one of the four plot types else interactive choice via “ask”.
Author(s)
Alexios Ghalanos
See Also
Classes uGARCHforecast
, uGARCHfit
and
uGARCHspec
.
Examples
## Not run:
data(sp500ret)
spec = ugarchspec(variance.model=list(model="gjrGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(1,1), arfima=FALSE, include.mean=TRUE,
archm = FALSE, archpow = 1), distribution.model="std")
fit = ugarchfit(data=sp500ret[, 1, drop = FALSE], out.sample = 0,
spec = spec, solver = "solnp")
dist = ugarchdistribution(fit, n.sim = 2000, n.start = 50, m.sim = 5)
## End(Not run)