GarchOxInterface           package:fSeries           R Documentation

_R _I_n_t_e_r_f_a_c_e _f_o_r _G_a_r_c_h _O_x

_D_e_s_c_r_i_p_t_i_o_n:

     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:

       1  garch   generalized AR conditional heteroskedastic models,
       2  egarch  exponential GARCH models,
       3  aparch  asymmetretic power ARCH models.

_U_s_a_g_e:

     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, ...)

_A_r_g_u_m_e_n_t_s:

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. 

 control: a list of additional control parameters:
           'truncation' - the number of truncation points,by default
          100, 
           'xscale' - should the time series be scaled by the standard
          deviation ? 

description: a character string which allows for a brief description. 

  digits: the number of digits to be printed. 

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. 

formula.var: formula object which specifies the variance. Use
          '~garch(p,q)' to specify GARCH(p,q) models. 

include.mean: should the mean be included? By default TRUE. 

  object: an object of class 'garchOx' as returned from the function
          code{garchOxFit}. 

  series: the time series to be modeled. The time series can be scaled
          settting 'control$xcale=TRUE'. 

   title: a character string which allows for a project title. 

   trace: a logical. Trace optimizer output? By default TRUE. 

       x: an object of class 'garchOx' as returned from the function
          'garchOxFit'. 

     ...: additional arguments to be passed to the 'print',  'summary',
          and 'plot' methods. 

_D_e_t_a_i_l_s:

     *Ox Interface:* 
           The function '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. 

     *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:\Ox\" and to unzip the OxGarch
     Package in the directory  "C:\Ox\Packages".  

     *Distribution:* 
      Ox and G@RCH are distributed by Timberlake Consultants Ltd.
     Timberlake  Consultants can be contacted through the following web
     site:  _www.timberlake.co.uk_. 

     *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:\Ox\lib". 

     *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". 

     *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.

_A_u_t_h_o_r(_s):

     Jurgen A. Doormik for the Ox Environment, _www.doornik.com_, 
       Sebastian Laurent for the Ox Garch package, _www.garch.org_, 
      Diethelm Wuertz for R's Ox Garch interface.

_R_e_f_e_r_e_n_c_e_s:

     Doornik J.A. (2002),  Object-Oriented Matrix Programming Using Ox,
      London, 3rd ed.: Timberlake Consultants Press and Oxford: 
     _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

_E_x_a_m_p_l_e_s:

     ## 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)

