print

Class: egarch

Display parameter estimation results for EGARCH models

Syntax

print(fit,VarCov)

Description

print(fit,VarCov) displays parameter estimates, standard errors, and t statistics for a fitted EGARCH model.

Input Arguments

fit

Estimated egarch model object, as output by estimate.

VarCov

Estimation error variance-covariance matrix, as output by estimate. VarCov is a square matrix with a row and column for each parameter known to the optimizer when model was fit. Known parameters include all parameters estimated as well as all parameters held fixed during optimization. Rows and columns associated with any parameters held fixed contain 0s.

The parameters in VarCov are ordered as follows:

  • Constant

  • Nonzero GARCH coefficients at positive lags

  • Nonzero ARCH coefficients at positive lags

  • Nonzero leverage coefficients at positive lags

  • Degrees of freedom (t innovation distribution only)

  • Offset (models with nonzero offset only)

Examples

expand all

Print EGARCH Estimation Results

Print the results from estimating an EGARCH model using simulated data.

Simulate data from an EGARCH(1,1) model with known parameter values.

modSim = egarch('Constant',0.01,'GARCH',0.8,'ARCH',0.14,...
             'Leverage',-0.1);
rng 'default';
[V,Y] = simulate(modSim,100);

Fit an EGARCH(1,1) model to the simulated data, turning off the print display.

model = egarch(1,1);
[fit,VarCov] = estimate(model,Y,'print',false);

Print the estimation results.

print(fit,VarCov)
 
    EGARCH(1,1) Conditional Variance Model:
    --------------------------------------
    Conditional Probability Distribution: Gaussian

                                  Standard          t     
     Parameter       Value          Error       Statistic 
    -----------   -----------   ------------   -----------
     Constant      0.0654888     0.0746316       0.877494
     GARCH{1}       0.858069      0.154361        5.55886
      ARCH{1}        0.27702      0.171036        1.61966
  Leverage{1}      -0.179034      0.125057       -1.43162
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