| 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)