uGARCHfilter-class {rugarch} | R Documentation |
Class for the univariate GARCH filter.
Class "GARCHfilter"
, directly.
Class "rGARCH"
, by class "GARCHfilter", distance 2.
signature(x = "uGARCHfilter")
:
extracts the position (dates), data, fitted values, residuals and
conditional sigma.
signature(object = "uGARCHfilter")
:
extracts the fitted values.
signature(object = "uGARCHfilter")
:
extracts the residuals.
signature(object = "uGARCHfilter")
:
extracts the conditional sigma values.
signature(object = "uGARCHfilter")
:
extracts the coefficients.
signature(object = "uGARCHfilter")
:
calculates and returns various information criteria.
signature(object = "uGARCHfilter")
:
calculates and returns the news impact curve.
signature(object = "uGARCHfilter")
:
extracts the likelihood.
signature(object = "uGARCHfilter")
:
calculates and returns the sign bias test of Engle and Ng (1993).
signature(object = "uGARCHfilter", groups = "numeric")
:
calculates and returns the adjusted goodness of fit statistic and p-values
for the fitted distribution based on the Vlaar and Palm paper (1993).
Groups is a numeric vector of bin sizes.
signature(object = "uGARCHfilter", pars = "missing",
distribution = "missing", model = "missing", submodel = "missing")
:
calculates and returns the persistence of the garch filter model.
signature(object = "uGARCHfilter", pars = "missing",
distribution = "missing", model = "missing")
:
calculates and returns the halflife of the garch fit variance given a
uGARCHfilter
object.
signature(object = "uGARCHfilter")
:
calculates and returns the unconditional mean of the conditional mean
equation (constant, ARMAX, arch-in-mean).
signature(object = "uGARCHfilter", pars = "missing",
distribution = "missing", model = "missing", submodel = "missing")
:
calculates and returns the long run unconditional variance of the garch
filter given a uGARCHfilter
object.
signature(x = "uGARCHfilter", y = "missing")
:
filter plots
signature(object = "uGARCHfilter")
:
filter summary.
The uGARCHfilter
class contains almost all the methods
available with the uGARCHfit
with the exception of those
requiring the scores of the likelihood (i.e the optimization process) such as
the nyblom test.
Alexios Ghalanos
## Not run: data(dji30ret) ctrl = list(rho = 1, delta = 1e-8, outer.iter = 100, inner.iter = 650, tol = 1e-6) spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1)), mean.model = list(armaOrder = c(1,1), include.mean = TRUE), distribution.model = "std") sgarch.fit = ugarchfit(data = dji30ret[,"AA",drop=FALSE], spec = spec, solver = "solnp", solver.control = ctrl) spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1)), mean.model = list(armaOrder = c(1,1), include.mean = TRUE), distribution.model = "std", fixed.pars = as.list(coef(sgarch.fit))) sgarch.filter = ugarchfilter(data = dji30ret[,"AA",drop=FALSE], spec = spec) c(likelihood(sgarch.filter), likelihood(sgarch.fit)) c(uncmean(sgarch.filter), uncmean(sgarch.fit)) c(uncvariance(sgarch.filter), uncvariance(sgarch.fit)) ## End(Not run)