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\name{GarchOxInterface}
\alias{GarchOxInterface}
\alias{garchOxFit}
\alias{print.garchOx}
\alias{summary.garchOx}
\alias{plot.garchOx}
\title{R Interface for Garch Ox}
\description{
A collection and description of functions to
fit the parameters of an univariate time
series to GARCH models interfacing the G@RCH
Ox Package.
\cr
The family of GARCH time series models includes the following
processes:
\tabular{rll}{
1 \tab garch \tab generalized AR conditional heteroskedastic models, \cr
2 \tab egarch \tab exponential GARCH models, \cr
3 \tab aparch \tab asymmetretic power ARCH models. }
}
\usage{
garchOxFit(formula, data, cond.dist = c("gaussian", "t", "ged", "skewed-t"),
include.mean = TRUE, trace = TRUE, control = list(), title = NULL,
description = NULL)
\method{print}{garchOx}(x, digits, \dots)
\method{summary}{garchOx}(object, \dots)
\method{plot}{garchOx}(x, \dots)
}
\arguments{
\item{cond.dist}{
a character string describing the distribution of innovations.
By default the optimization is based on gaussian log likelihood
parameter optimization denoted by "gaussian". Alternatively, a
Student-t "t", a generalized error "sged", or a skewed
Student-t "skewed-t" can be chosen.
}
\item{control}{
a list of additional control parameters:\cr
\code{truncation} - the number of truncation points,by default
100, \cr
\code{xscale} - should the time series be scaled by the
standard deviation ?
}
\item{data}{
an optional timeSeries or data frame object containing the variables
in the model. If not found in \code{data}, the variables are taken
from \code{environment(formula)}, typically the environment from which
\code{armaFit} is called. If \code{data} is an univariate series, then
the series is converted into a numeric vector and the name of the
response in the formula will be neglected.
}
\item{description}{
a character string which allows for a brief description.
}
\item{digits}{
the number of digits to be printed.
}
\item{formula}{
[garchFit] - \cr
formula object describing the mean and variance equation of the
ARMA-GARCH/APARCH model. A pure GARCH(1,1) model is selected
when e.g. \code{formula=~garch(1,1)}. To specify for example an
ARMA(2,1)-APARCH(1,1) use \code{formula = ~arma(2,1)+apaarch(1,1)}.
}
\item{include.mean}{
should the mean be included? By default TRUE.
}
\item{object}{
an object of class \code{garchOx} as returned from the function
code{garchOxFit}.
}
\item{title}{
a character string which allows for a project title.
}
\item{trace}{
a logical flag. Should the estimation process be ttraced?
By default TRUE.
}
\item{x}{
an object of class \code{garchOx} as returned from the function
\code{garchOxFit}.
}
\item{\dots}{
additional arguments to be passed to the \code{print},
\code{summary}, and \code{plot} methods.
}
}
\details{
\bold{Ox Interface:}
\cr
The function \code{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.
\cr
\bold{About Ox:}
\cr
Ox (tm) is an object-oriented matrix language with a comprehensive
mathematical and statistical function library. Many packages were
written for Ox including software mainly for econometric modelling.
The Ox packages for time series analysis and forecasting, Arfima,
Doornik and Ooms [2003], Garch, Laurent and Peters [2005], and State
Space Modelling, Koopman, Shepard and Doornik [1998], are especially worth
to note. Since most of the R-users wan't to change to another Statistical
Computing environment, we made selected parts of the G@RCH Ox software
available for them through an R-Interface. What you have to do, is
to read carefully the "Ox citation and copyright" rules and if you
agree and fullfill the conditions, then download the OxConsole Software
together with the "OxGarch" Package, currently G@RCH 4.0. If you are
not qualified for a free license, order your copy from Timberlake
Consultants. We recommend to install the "Setup.exe" under the path
"C:\\Ox\\Ox3" and to unzip the OxGarch Package in the directory
"C:\\Ox\\Ox3\\Packages". An Update to Ox4 has not yet be done.
\cr
\bold{Distribution:}
\cr
Ox and G@RCH are distributed by Timberlake Consultants Ltd. Timberlake
Consultants can be contacted through the following web site:
\emph{www.timberlake.co.uk}.
\cr
\bold{Installation of the Interface:}
\cr
In addition you have to copy the file "GarchOxModelling.ox" (which
is the interface written especially for Rmetrics) from
the "fSeries/ox/" directory to the Ox library directory
"C:\\Ox\\lib".
\cr
\bold{Ox Citation and Copyright Rules:}
\cr
Ox and all its components are copyright of Jurgen A. Doornik. The
Console (command line) versions may be used freely for academic
research and teaching purposes only. Commercial users and others
who do not qualify for the free version must purchase the Windows
version of Ox and GiveWin with documentation, regardless of which
version they use (so even when only using Ox on Linux or Unix).
Ox should be cited whenever it is used. Refer to the two references
given below. Note, failure to cite the use of Ox in published work
may result in loss of the right to use the free version, and an
invoice at the full commercial price. Ox is available from Timberlake
Consultants. The Ox syntax is public, and you may do with your own
Ox code whatever you wish, including the file "GarchOxModelling.ox".
\cr
\bold{Work to do:}
\cr
Note, only a small part of the functionalities are interfaced until
now to R. But, principally it would be possible to interface also other
functionalities offered by the Ox Garch Package. This work is left
to the Ox/Rmetrics user.
}
\references{
Doornik J.A. (2002),
Object-Oriented Matrix Programming Using Ox,
London, 3rd ed.: Timberlake Consultants Press and Oxford:
\emph{www.doornik.com}.
Doornik J.A., Ooms M. (2003),
Computational Aspects of Maximum Likelihood Estimation of
Autoregressive Fractionally Integrated Moving Average Models,
Computational Statistics and Data Analysis 42, 333--348.
Koopman J.S., Shepard N., Doornik J.A. (1999),
Statistical Algorithms for Models in State Space using SsfPack 2.2,
Econometrics Journal 2, 113--166.
Laurent S., Peters J.P. (2002);
G@RCH 2.2: An Ox Package for Estimating and Forecasting Various ARCH Models,
Journal of Economic Surveys 16, 447--485.
Laurent S., Peters J.P., [2005],
G@RCH 4.0, Estimating and Forecasting ARCH Models,
Timberlake Consultants, www.timberlake.co.uk
}
\author{
Jurgen A. Doormik for the Ox Environment, \emph{www.doornik.com}, \cr
Sebastian Laurent for the Ox Garch package, \emph{www.garch.org}, \cr
Diethelm Wuertz for R's Ox Garch interface.
}
\examples{
\dontrun{
## Load Benchmark Data Set:
data(dem2gbp)
x = dem2gbp[, 1]
## garchOxFit -
# Fit GARCH(1,1):
garchOxFit(formula = ~arma(0,0) + ~garch(1,1))
}
}
\keyword{models}
|
