# Gradient of the GARCH part of the log-likelihood function of an (E)DCC GARCH model

### Description

This function returns the gradient of the volatility part of the log-likelihood function of the DCC.

### Usage

1 |

### Arguments

`par` |
a vector of the parameters in the vector GARCH equation |

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

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

### Value

A vector of the gradient. *(3N \times 1)* for `"diagonal"`

and *(2N^{2}+N \times 1)* for `"extended"`

.

### Note

The function can be called from `optim`

in `dcc.estimation1`

. For obtaining
the gradient for all *t*, use `dlv`

instead.

### 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.

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

### See Also

`dcc.estimation1`

,
`dlv`