simulateDCC: Simulating data from a DCC-GARCH process
In ccgarch2: Conditional Correlation GARCH Models

Description Usage Arguments Details Value References See Also Examples

This function simulates data from a DCC-GARCH process

1	simulateDCC(a, b, Q, a0, A, B, nobs, ncut=1000)

`a`	a constant in the DCC part.
`b`	a constant in the DCC part.
`Q`	a covariance matrix of the standardized residuals.
`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.

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 DCC-GARCH model, estimateDCC is available.

This function returns a list with the following components.

`DCC`	a matrix of the simulated DCC
`z`	a matrix of the simulated standardized residuals
`Q`	a matrix of the simulated Q_t, which is transformed into DCC.
`h`	a matrix of the simulated conditional variances.
`eps`	a matrix of the simulated time series with DCC-GARCH errors.

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.

estimateDCC, summary.dcc