\HeaderA{GarchOxInterface}{R Interface for Garch Ox}{GarchOxInterface}
\aliasA{garchOxFit}{GarchOxInterface}{garchOxFit}
\aliasA{plot.garchOx}{GarchOxInterface}{plot.garchOx}
\aliasA{print.garchOx}{GarchOxInterface}{print.garchOx}
\aliasA{summary.garchOx}{GarchOxInterface}{summary.garchOx}
\keyword{models}{GarchOxInterface}
\begin{Description}\relax
A collection and description of functions to 
fit the parameters of an univariate time 
series to GARCH models interfacing the G@RCH
Ox Package. 
\\

The family of GARCH time series models includes the following 
processes:

\Tabular{rll}{
1       & garch      &  generalized AR conditional heteroskedastic models, \\
2       & egarch &  exponential GARCH models, \\
3       & aparch &  asymmetretic power ARCH models. }
\end{Description}
\begin{Usage}
\begin{verbatim}
garchOxFit(formula.mean = ~ arma(0, 0), formula.var = ~ garch(1, 1), 
        series = x, cond.dist = c("gaussian", "t", "ged", "skewed-t"), 
        include.mean = TRUE, trace = TRUE, control = list(), title = NULL,
        description = NULL)
        
## S3 method for class 'garchOx':
print(x, digits, ...)
## S3 method for class 'garchOx':
summary(object, ...)
## S3 method for class 'garchOx':
plot(x, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{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[\code{control}] a list of additional control parameters:\\
\code{truncation} - the number of truncation points,by default
100, \\
\code{xscale} - should the time series be scaled by the
standard deviation ?

\item[\code{description}] a character string which allows for a brief description.

\item[\code{digits}] the number of digits to be printed.

\item[\code{formula.mean}] formula object which specifies the mean. Pure AR and MA models 
can be specified as ARMA(0,q) and ARMA(p,0) respectively.

\item[\code{formula.var}] formula object which specifies the variance. Use \code{\textasciitilde{}garch(p,q)}
to specify GARCH(p,q) models.

\item[\code{include.mean}] should the mean be included? By default TRUE.

\item[\code{object}] an object of class \code{garchOx} as returned from the function
code{garchOxFit}.

\item[\code{series}] the time series to be modeled. The time series can be scaled
settting \code{control\$xcale=TRUE}.

\item[\code{title}] a character string which allows for a project title.

\item[\code{trace}] a logical. Trace optimizer output? By default TRUE.

\item[\code{x}] an object of class \code{garchOx} as returned from the function
\code{garchOxFit}.

\item[\code{...}] additional arguments to be passed to the \code{print}, 
\code{summary}, and \code{plot} methods.

\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\bold{Ox Interface:}
\\     
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.
\\

\bold{About Ox:}
\\
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:\bsl{}Ox\bsl{}" and to unzip the OxGarch Package in the directory 
"C:\bsl{}Ox\bsl{}Packages". 
\\

\bold{Distribution:}
\\
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}.
\\

\bold{Installation of the Interface:}
\\
In addition you have to copy the file "GarchOxModelling.ox" (which 
is the interface written especially for Rmetrics) from 
the "fSeries/data/" directory to the Ox library directory 
"C:\bsl{}Ox\bsl{}lib".
\\

\bold{Ox Citation and Copyright Rules:}
\\
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".
\\

\bold{Work to do:}
\\
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.
\end{Details}
\begin{Author}\relax
Jurgen A. Doormik for the Ox Environment, \emph{www.doornik.com}, \\ 
Sebastian Laurent for the Ox Garch package, \emph{www.garch.org}, \\
Diethelm Wuertz for R's Ox Garch interface.
\end{Author}
\begin{References}\relax
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
\end{References}
\begin{Examples}
\begin{ExampleCode}
## SOURCE("fSeries1.34D-GarchOxModelling")

## Not run: 
## garchOxFit -
   # Load Benchmark Data Set:
   data(dem2gbp)
   x = dem2gbp[, 1]
   # Fit GARCH(1,1):
   garchOxFit(formula.mean = ~arma(0,0), formula.var = ~garch(1,1))
## End(Not run)
\end{ExampleCode}
\end{Examples}

