garchSpec               package:fGarch               R Documentation

_U_n_i_v_a_r_i_a_t_e _G_A_R_C_H _T_i_m_e _S_e_r_i_e_s _S_p_e_c_i_f_i_c_a_t_i_o_n

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

     Specifies an univariate GARCH time series model.

_U_s_a_g_e:

     garchSpec(model = list(omega = 1.0e-6, alpha = 0.1, beta = 0.8), 
         presample = NULL, cond.dist = c("rnorm", "rged", "rstd", "rsnorm", 
         "rsged", "rsstd"), rseed = NULL)
         
     show.garchSpec(object)

_A_r_g_u_m_e_n_t_s:

cond.dist: a character string naming the desired conditional
          distribution. Valid values are '"dnorm"', '"dged"', '"dstd"',
           '"dsnorm"', '"dsged"', '"dsstd"'. The default value  is the
          normal distribution. 

   model: a list of GARCH model parameters: 
           'omega' - the constant coefficient of the variance equation,
          by default 1e-6; 
           'alpha' - the value or vector of autoregressive
          coefficients,  by default 0.1, specifying a model of order 1; 
           'beta' - the value or vector of variance coefficients, by
          default 0.8, specifying a model of order 1;  
           The optional values for the linear part are: 
           'mu' - the mean value, by default 0; 
           'ar' - the autoregressive ARMA coefficients, by default 0; 
           'ma' - the moving average ARMA coefficients, by default 0.  
           The optional parameters for the conditional distributions
          are:
           'skew' - the skewness parameter (also named xi), by default
          0.9, effective only for the '"dsnorm"', the '"dsged"', and
          the '"dsstd"' skewed conditional distributions; 
           'shape' = the shape parameter (also named nu), by default 2 
          for the '"dged"' and '"dsged"', and by default 4 for the
          '"dstd"' and '"dsstd"' conditional distributions.

           Note, the default model specifies Bollerslev's GARCH(1,1)
          model with normal distributed innovations. 

  object: an object of class 'garchSpec' as returned from the function
          'garchSpec()'. 

presample: a numeric three column matrix with start values for the
          series,  for the innovations, and for the conditional
          variances. For an  ARMA(m,n)-GARCH(p,q) process the number of
          rows must be at least  max(m,n,p,q), longer presamples are
          cutted. 

   rseed: single integer argument, the seed for the intitialization of
          the random number generator for the innovations. 

_V_a_l_u_e:

     'garchSpec'

      returns a S4 object of class 'garchSpec' with the following
     slots:

   @call: the call of the 'garch' function. 

@formula: a list with two formula entries for the mean and variance 
          equation. 

  @model: a list with the model parameters. 

@presample: a numeric matrix with presample values. 

@distribution: a character string with the name of the conditional
          distribution. 

  @rseed: an integer with the random number generator seed. 

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

     Diethelm Wuertz for the Rmetrics R-port.

_E_x_a_m_p_l_e_s:

     ## garchSpec -

        # Normal Conditional Distribution:
        spec = garchSpec()
        spec
        
        # Skewed Normal Conditional Distribution:
        spec = garchSpec(model = list(skew = 0.8), cond.dist = "rsnorm")
        spec
        
        # Skewed GED Conditional Distribution:
        spec = garchSpec(model = list(skew = 0.9, shape = 4.8), cond.dist = "rsged")
        spec

