Description Usage Arguments Value References See Also

This function returns the analytical partial derivatives of the volatility part of
the log-likelihood function of the DCC-GARCH model.
The function is called from `dcc.results`

.

1 | ```
dlv(u, a, A, B, model)
``` |

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

`a` |
a vector of the constants in the volatility part |

`A` |
an ARCH parameter matrix |

`B` |
a GARCH parameter matrix |

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

A matrix of partial derivatives. *(T \times npar.h)* where *npar.h* stand for
the number of parameters in the GARCH part, *npar.h = 3N* for `"diagonal"`

and
*npar.h = 2N^{2}+N* for `"extended"`

.

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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