(To be removed) Display parameter estimation results for conditional variance models
print
will be removed in a future release. Use summarize
instead.
Syntax
Description
Examples
Print GARCH Estimation Results
Print the results from estimating a GARCH model using simulated data.
Simulate data from an GARCH(1,1) model with known parameter values.
Mdl0 = garch('Constant',0.01,'GARCH',0.8,'ARCH',0.14)
Mdl0 = garch with properties: Description: "GARCH(1,1) Conditional Variance Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 1 Q: 1 Constant: 0.01 GARCH: {0.8} at lag [1] ARCH: {0.14} at lag [1] Offset: 0
rng 'default';
[V,Y] = simulate(Mdl0,100);
Fit a GARCH(1,1) model to the simulated data, turning off the print display.
Mdl = garch(1,1); [EstMdl,EstParamCov] = estimate(Mdl,Y,'Display','off');
Print the estimation results.
print(EstMdl,EstParamCov)
Warning: PRINT will be removed in a future release; use SUMMARIZE instead.
GARCH(1,1) Conditional Variance Model: ---------------------------------------- Conditional Probability Distribution: Gaussian Standard t Parameter Value Error Statistic ----------- ----------- ------------ ----------- Constant 0.0167004 0.0165077 1.01167 GARCH{1} 0.77263 0.0776905 9.94498 ARCH{1} 0.191686 0.0750675 2.55351
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.
Mdl0 = egarch('Constant',0.01,'GARCH',0.8,'ARCH',0.14,... 'Leverage',-0.1); rng 'default'; [V,Y] = simulate(Mdl0,100);
Fit an EGARCH(1,1) model to the simulated data, turning off the print display.
Mdl = egarch(1,1); [EstMdl,EstParamCov] = estimate(Mdl,Y,'Display','off');
Print the estimation results.
print(EstMdl,EstParamCov)
Warning: PRINT will be removed in a future release; use SUMMARIZE instead.
EGARCH(1,1) Conditional Variance Model: -------------------------------------- Conditional Probability Distribution: Gaussian Standard t Parameter Value Error Statistic ----------- ----------- ------------ ----------- Constant 0.0654887 0.0746315 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
Print GJR Estimation Results
Print the results from estimating a GJR model using simulated data.
Simulate data from a GJR(1,1) model with known parameter values.
Mdl0 = gjr('Constant',0.01,'GARCH',0.8,'ARCH',0.14,... 'Leverage',0.1); rng 'default'; [V,Y] = simulate(Mdl0,100);
Fit a GJR(1,1) model to the simulated data, turning off the print display.
Mdl = gjr(1,1); [EstMdl,EstParamCov] = estimate(Mdl,Y,'Display','off');
Print the estimation results.
print(EstMdl,EstParamCov)
Warning: PRINT will be removed in a future release; use SUMMARIZE instead.
GJR(1,1) Conditional Variance Model: -------------------------------------- Conditional Probability Distribution: Gaussian Standard t Parameter Value Error Statistic ----------- ----------- ------------ ----------- Constant 0.194785 0.254199 0.766271 GARCH{1} 0.69954 0.11266 6.20928 ARCH{1} 0.192965 0.0931335 2.07192 Leverage{1} 0.214988 0.223923 0.9601
Input Arguments
EstParamCov
— Estimated parameter variance-covariance matrix
numeric matrix
Estimated parameter variance-covariance matrix, returned as a numeric matrix.
EstParamCov
is usually the estimated conditional
variance model returned by estimate
.
The rows and columns associated with any parameters contain the covariances. The standard errors of the parameter estimates are the square root of the entries along the main diagonal.
The rows and columns associated with any parameters held fixed as equality
constraints during estimation contain 0
s.
The order of the parameters in EstParamCov
must
be:
Constant
Nonzero GARCH coefficients at positive lags
Nonzero ARCH coefficients at positive lags
For EGARCH and GJR models, nonzero leverage coefficients at positive lags
Degrees of freedom (t innovation distribution only)
Offset (models with nonzero offset only)
Data Types: double
Version History
Introduced in R2012a
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