garch-methods            package:tseries            R Documentation

_M_e_t_h_o_d_s _f_o_r _F_i_t_t_e_d _G_A_R_C_H _M_o_d_e_l_s

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

     Methods for fitted GARCH model objects.

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

     ## S3 method for class 'garch':
     predict(object, newdata, genuine = FALSE, ...)
     ## S3 method for class 'garch':
     coef(object, ...)
     ## S3 method for class 'garch':
     residuals(object, ...)
     ## S3 method for class 'garch':
     fitted(object, ...)
     ## S3 method for class 'garch':
     print(x, digits = max(3, getOption("digits") - 3), ...)
     ## S3 method for class 'garch':
     plot(x, ask = interactive(), ...)
     ## S3 method for class 'garch':
     logLik(object, ...)

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

object, x: an object of class '"garch"'; usually, a result of a call to
          'garch'.

 newdata: a numeric vector or time series to compute GARCH predictons. 
          Defaults to 'eval(parse(text=object$series))'.

 genuine: a logical indicating whether a genuine prediction should be
          made, i.e., a prediction for which there is no target
          observation available.

  digits: see 'printCoefmat'.

     ask: Should the 'plot' method work interactively?  See
          'interactive'.

     ...: further arguments passed to or from other methods.

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

     'predict' returns +/- the conditional standard deviation
     predictions from a fitted GARCH model.

     'coef' returns the coefficient estimates.

     'residuals' returns the GARCH residuals, i.e., the time series
     used to fit the model divided by the computed conditional standard
     deviation predictions for this series. Under the assumption of
     conditional normality the residual series should be i.i.d.
     standard normal.  

     'fitted' returns +/- the conditional standard deviation
     predictions for the series which has been used to fit the model.

     'plot' graphically investigates normality and remaining ARCH
     effects for the residuals.

     'logLik' returns the log-likelihood value of the GARCH(p, q) model
     represented by 'object' evaluated at the estimated coefficients.
     It is assumed that first max(p, q) values are fixed.

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

     For 'predict' a bivariate time series (two-column matrix) of
     predictions. 

     For 'coef', a numeric vector, for 'residuals' and 'fitted' a
     univariate (vector) and a bivariate time series (two-column
     matrix), respectively.

     For 'plot' and 'print', the fitted GARCH model object.

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

     A. Trapletti

