simulateCCC: Simulating data from a CCC-GARCH process

Description Usage Arguments Details Value References See Also Examples

View source: R/simulateCCC.R

Description

This function simulates data from a CCC-GARCH process

Usage

1
simulateCCC(R, a0, A, B, nobs, ncut=1000)

Arguments

R

a constant conditional correlation matrix.

a0

a vector of constants in the GARCH part.

A

a matrix of ARCH parameter.

B

a matrix of GARCH parameter.

nobs

the number of observations to be simulated.

ncut

the number of initial entries to be discarded. Default is 1000.

Details

If the ARCH and GARCH parameter matrices, A and B, are set as non-diagonal, the corresponding DGP allows for interactions in conditional variances (see Nakatani and Ter\"asvirta (2009) for instance).

For estimating a CCC-GARCH model, estimateCCC is available.

Value

This function returns a list with the following components.

z

a matrix of the simulated standardized residuals

h

a matrix of the simulated conditional variances.

eps

a matrix of the simulated time series with CCC-GARCH errors.

References

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.

Nakatani, T. and T. Ter\"asvirta (2009), “Testing for Volatility Interactions in the Constant Conditional Correlation GARCH Model”, Econometrics Journal, 12, 147–163.

See Also

estimateCCC, summary.ccc

Examples

1
## See examples in "estimateCCC".

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