summary-methods            package:fGarch            R Documentation

_G_A_R_C_H _S_u_m_m_a_r_y _M_e_t_h_o_d_s

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

     Summary methods for GARCH Modelling.

_M_e_t_h_o_d_s:



     _o_b_j_e_c_t = "_A_N_Y" Generic function

     _o_b_j_e_c_t = "_f_G_A_R_C_H" Summary function for objects of class
          '"fGARCH"'.


_H_o_w _t_o _r_e_a_d _a _d_i_a_g_n_o_s_t_i_c _s_u_m_m_a_r_y _r_e_p_o_r_t?:

     The first five sections return the title, the call, the mean and
     variance formula, the conditional distribution and the type of
     standard errors:


             Title:
              GARCH Modelling 

             Call:
              garchFit(~ garch(1, 1), data = garchSim(), trace = FALSE) 

             Mean and Variance Equation:
              ~arch(0)

             Conditional Distribution:
              norm 

             Std. Errors:
              based on Hessian

     The next three sections return the estimated coefficients, and an
     error analysis including standard errors, t values, and
     probabilities, as well as the log Likelihood values from 
     optimization:


             Coefficient(s):
                       mu         omega        alpha1         beta1  
             -5.79788e-05   7.93017e-06   1.59456e-01   2.30772e-01  

             Error Analysis:
                      Estimate  Std. Error  t value Pr(>|t|)
             mu     -5.798e-05   2.582e-04   -0.225    0.822
             omega   7.930e-06   5.309e-06    1.494    0.135
             alpha1  1.595e-01   1.026e-01    1.554    0.120
             beta1   2.308e-01   4.203e-01    0.549    0.583

             Log Likelihood:
              -843.3991    normalized:  -Inf 

     The next section provides results on standardized residuals tests,
     including statistic and p values, and on information criterion
     statistic including AIC, BIC, SIC, and HQIC:


             Standardized Residuals Tests:
                                             Statistic p-Value    
              Jarque-Bera Test   R    Chi^2  0.4172129 0.8117146  
              Shapiro-Wilk Test  R    W      0.9957817 0.8566985  
              Ljung-Box Test     R    Q(10)  13.05581  0.2205680  
              Ljung-Box Test     R    Q(15)  14.40879  0.4947788  
              Ljung-Box Test     R    Q(20)  38.15456  0.008478302
              Ljung-Box Test     R^2  Q(10)  7.619134  0.6659837  
              Ljung-Box Test     R^2  Q(15)  13.89721  0.5333388  
              Ljung-Box Test     R^2  Q(20)  15.61716  0.7400728  
              LM Arch Test       R    TR^2   7.049963  0.8542942  

             Information Criterion Statistics:
                      AIC      BIC      SIC     HQIC 
                 8.473991 8.539957 8.473212 8.500687  


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

     Diethelm Wuertz for the Rmetrics R-port.

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

     ## garchSim -
        x = garchSim(n = 200)

     ## garchFit - 
        fit = garchFit(formula = x ~ garch(1, 1), data = x, trace = FALSE)
        summary(fit)

