summary.ccc: Summarizing CCC-GARCH estimation

In ccgarch2: Conditional Correlation GARCH Models

Description

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

Usage

## S3 method for class 'ccc'
summary(object, ...)

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

Arguments

object	an object of class "dcc".
x	an object of class "summary.dcc".
digits	a number of digits to display.
...	optional arguments. Currently not in use.

Details

The function summary.dcc() computes and returns a list of summary statistics of the fitted model in object of the "dcc" 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 "ccc" class, the following list components are added.

nobs	the number of observations.
mu	the estimate of the conditional mean.
R	the estimates of the conditional correlations. Corresponding to R[lower.tri(R)].
garch.par	the estimates of the parameters in the GARCH part.
coef	the table of all estimates with robust s.e. and associated inferencial statistics.
convergence	Integer code for the convergence status of the optimization. See the help of optim for detail.
counts	a matrix containing the number of calls to the objective function and its (numerical) gradient during the optimization. See the help of optim 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 ‘ccc’ 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

Bollerslev, T. (1990), “Modeling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Approach”, Review of Economics and Statistics, 72, 498–505.

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

estimateCCC

ccgarch2 documentation built on May 2, 2019, 5:56 p.m.