summary.cdcc: Summarizing corrected DCC-GARCH estimation

Description Usage Arguments Details Value Note References See Also

View source: R/cDCC.R

Description

These functions are S3 methods for class "cdcc" or "summary.cdcc".

Usage

1
2
3
4
5
## S3 method for class 'cdcc'
summary(object, ...)

## S3 method for class 'summary.cdcc'
print(x, digits = max(3, getOption("digits") - 1), ...)

Arguments

object

an object of class "cdcc".

x

an object of class "summary.cdcc".

digits

a number of digits to display.

...

optional arguments. Currently not in use.

Details

The function summary.cdcc() computes and returns a list of summary statistics of the fitted model in object of the "cdcc" class.

Some of the values are printed up to certain decimal places. Actual values of individual components are displayed separately, for instance, by summary(object)$coefficients. See the Value section for a list of components.

For residual diagnostics, residDiag carries out the Jarque-Bera test of normality for the standardized residuals and the Ljung-Box test for serial correlations in the standardized and squared standardized residuals.

Value

In addition to those available in the object of the "cdcc" class, the following list components are added.

nobs

the number of observations.

mu

the estimate of the conditional mean.

garch.par

the estimates of the parameters in the GARCH part.

cDcc.par

the estimates of the parameters in the cDCC part.

coef

the table of all estimates with robust s.e. and associated inferencial statistics.

convergence

Integer codes for the convergence status of the 1st and 2nd stage optimization. See the help of optim and constrOptim for detail.

counts

a matrix containing the number of calls to the objective function and its (numerical) gradient during the 1st and 2nd stage optimization. The number of calls to the gradient in the 2nd stage is "NA" because it does not use it. See the help of optim and constrOptim for detail.

logLik

a value of the lig-likelihood function at the estimates.

AIC

Akaike's information criterion.

BIC

Bayesian information criterion.

CAIC

Consistent Akaike's information criterion.

Note

It is time consuming to implement the summary method for ‘cdcc’ class object. This is because obtaining numerical derivatives, which are used for computing the (robust) standard errors of the parameter estimates, is computationally demanding.

References

Aielli, G.P. (2013), “Dynamic Conditional Correlation: On Properties and Estimation.” Journal of Business and Economic Statistics 31, 282–299.

Engle, R.F. and K. Sheppard (2001), “Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH.” Stern Finance Working Paper Series FIN-01-027 (Revised in Dec. 2001), New York University Stern School of Business.

Engle, R.F. (2002), “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business and Economic Statistics 20, 339–350.

See Also

estimateCDCC


ccgarch2 documentation built on May 31, 2017, 4:23 a.m.