Description Usage Arguments Details Value References See Also Examples

This function simulates data from a CCC-GARCH process

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

`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. |

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.

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. |

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.

1 | ```
## See examples in "estimateCCC".
``` |

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