## S3 method for class 'garch':
predict(object, newdata, genuine = FALSE, \dots)
## S3 method for class 'garch':
coef(object, \dots)
## S3 method for class 'garch':
vcov(object, \dots)
## S3 method for class 'garch':
residuals(object, \dots)
## S3 method for class 'garch':
fitted(object, \dots)
## S3 method for class 'garch':
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'garch':
plot(x, ask = interactive(), ...)
## S3 method for class 'garch':
logLik(object, \dots)
"garch"
; usually, a result
of a call to garch
.eval(parse(text=object$series))
.printCoefmat
.plot
method work interactively? See
interactive
.predict
a bivariate time series (two-column matrix) of
predictions.
For coef
, a numeric vector, for residuals
and
fitted
a univariate (vector) and a bivariate time series
(two-column matrix), respectively. For plot
and print
, the fitted GARCH model object.
predict
returns +/- the conditional standard deviation
predictions from a fitted GARCH model. coef
returns the coefficient estimates.
vcov
the associated covariance matrix estimate (outer product of gradients estimator).
residuals
returns the GARCH residuals, i.e., the time series
used to fit the model divided by the computed conditional standard
deviation predictions for this series. Under the assumption of
conditional normality the residual series should be i.i.d. standard
normal.
fitted
returns +/- the conditional standard deviation
predictions for the series which has been used to fit the model.
plot
graphically investigates normality and remaining ARCH
effects for the residuals.
logLik
returns the log-likelihood value of the GARCH(p, q)
model represented by object
evaluated at the estimated
coefficients. It is assumed that first max(p, q) values are fixed.