# dlv: Gradient of the GARCH part of the log-likelihood function of... In ccgarch: Conditional Correlation GARCH models

## Description

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.

## Usage

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

## Arguments

 u a matrix of the data used for estimating an (E)DCC-GARCH model (T \times N) a a vector of the constants in the volatility part (N \times 1) A an ARCH parameter matrix (N \times N) B a GARCH parameter matrix (N \times N) model a character string describing the model."diagonal" for the diagonal model and "extended" for the extended (full ARCH and GARCH parameter matrices) model

## Value

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".

## 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.

dcc.estimation