This function computes various analytical derivatives of the second stage log-likelihood function (the DCC part) of the (E)DCC-GARCH model.

1 | ```
dlc(dcc.para, B, u, h, model)
``` |

`dcc.para` |
the estimates of the (E)DCC parameters |

`B` |
the estimated GARCH parameter matrix |

`u` |
a matrix of the used for estimating the (E)DCC-GARCH model |

`h` |
a matrix of the estimated conditional variances |

`model` |
a character string describing the model. |

a list with components:

`dlc` |
the gradient of the DCC log-likelihood function w.r.t. the DCC parameters |

`dvecP` |
the partial derivatives of the DCC matrix, |

`dvecQ` |
the partial derivatives of the |

`d2lc` |
the Hessian of the DCC log-likelihood function w.r.t. the DCC parameters |

`dfdwd2lc` |
the cross derivatives of the DCC log-likelihood function |

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.

Hafner, C.M. and H. Herwartz (2008),
“Analytical Quasi Maximum Likelihood Inference in Multivariate Volatility Models.”
*Metrika*
**67**, 219–239.

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