fGarch-package            package:fGarch            R Documentation

_G_A_R_C_H _M_o_d_e_l_l_i_n_g _P_a_c_k_a_g_e

_D_e_s_c_r_i_p_t_i_o_n:

     Package of econometric functions for modelling GARCH processes.

_D_e_t_a_i_l_s:


       Package:    fGarch
       Type:       Package
       Version:    270.73
       Date:       2008
       License:    GPL (>= 2)
       Copyright:  (c) 1999-2008 Diethelm Wuertz and Rmetrics Foundation
       URL:        <URL: http://www.rmetrics.org>

     GARCH, Generalized Autoregressive Conditional Heteroskedastic,
     models  have become important in the analysis of time series data,
     particularly  in financial applications when the goal is to
     analyze and forecast  volatility. 

     For this purpose, the family of GARCH functions offers functions
     for  simulating, estimating and forecasting various univariate
     GARCH-type  time series models in the conditional variance and an
     ARMA specification  in the conditional mean. The function
     '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.

_T_i_m_e _S_e_r_i_e_s _S_i_m_u_l_a_t_i_o_n:

     contains functions to simulate artificial GARCH and APARCH time
     series processes.

     Functions:

       'garchSpec'  Specifies an univariate GARCH time series model,
       'garchSim'   Simulates a GARCH/APARCH process.

_P_a_r_a_m_e_t_e_r _E_s_t_i_m_a_t_i_o_n:

     contains functions to fit the parameters of GARCH and APARCH  time
     series processes.

     Functions:

       '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.

_F_o_r_e_c_a_s_t_i_n_g:

     contains functions to forcecast mean and variance of GARCH and
     APARCH processes.

     Functions:

       'predict'  Forecasts from an object of class 'fGARCH'.

_S_t_a_n_d_a_r_d_i_z_e_d _D_i_s_t_r_i_b_u_t_i_o_n _F_u_n_c_t_i_o_n_s:

     contains functions to model standardized distribution functions.

     Functions:

       '[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.

_O_X _I_n_t_e_r_f_a_c_e:

     NOTE: garchOxFit is no longer part of fGarch package. If you are
     interested to use, please contact us.

     contains a Windows interface to OX.

     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.

_A_u_t_h_o_r(_s):

     Diethelm Wuertz and Rmetrics Core Team.

