garchSpec Specifies an univariate GARCH time series
model,
garchSim Simulates a GARCH/APARCH process. }garchFit Fits the parameters of a GARCH process,
residuals Extracts residuals from a fitted 'fGARCH'
object,
fitted Extracts fitted values from a fitted 'fGARCH'
object,
volatility Extracts conditional volatility from a fitted
'fGARCH' object,
coef Extracts coefficients from a fitted 'fGARCH'
object,
formula Extracts formula expression from a fitted
'fGARCH' object. }predict Forecasts from an object of class 'fGARCH'.
}[dpqr]norm Normal distribution function,
[dpqr]snorm Skew Normal distribution function,
[s]normFit Fits parameters of [skew] Normal
distribution,
[dpqr]ged Generalized Error distribution function,
[dpqr]sged Skew Generalized Error distribution
function,
[s]gedFit Fits parameters of [skew] Generalized Error
distribution,
[dpqr]std standardized Student-t distribution function,
[dpqr]sstd Skew standardized Student-t distribution
function,
[s]stdFit Fits parameters of [skew] Student-t
distribution,
absMoments Computes absolute Moments of these
distribution.
}garchOxFit interfaces a subset of the functionality
of the G@ARCH 4.0 Package written in Ox.
G@RCH 4.0 is one of the most sophisticated packages for modelling
univariate GARCH processes including GARCH, EGARCH, GJR, APARCH,
IGARCH, FIGARCH, FIEGARCH, FIAPARCH and HYGARCH models. Parameters
can be estimated by approximate (Quasi-) maximum likelihood methods
under four assumptions: normal, Student-t, GED or skewed Student-t
errors.garchFit is a numerical
implementation of the maximum log-likelihood approach under different
assumptions, Normal, Student-t, GED errors or their skewed versions.
The parameter estimates are checked by several diagnostic analysis tools
including graphical features and hypothesis tests. Functions to compute
n-step ahead forecasts of both the conditional mean and variance are also
available. The number of GARCH models is immense, but the most influential models
were the first. Beside the standard ARCH model introduced by Engle [1982]
and the GARCH model introduced by Bollerslev [1986], the function
garchFit also includes the more general class of asymmetric power
ARCH models, named APARCH, introduced by Ding, Granger and Engle [1993].
The APARCH models include as special cases the TS-GARCH model of
Taylor [1986] and Schwert [1989], the GJR-GARCH model of Glosten,
Jaganathan, and Runkle [1993], the T-ARCH model of Zakoian [1993], the
N-ARCH model of Higgins and Bera [1992], and the Log-ARCH model of
Geweke [1986] and Pentula [1986].
There exist a collection of review articles by Bollerslev, Chou and
Kroner [1992], Bera and Higgins [1993], Bollerslev, Engle and
Nelson [1994], Engle [2001], Engle and Patton [2001], and Li, Ling
and McAleer [2002] which give a good overview of the scope of the
research.