dlv.est: 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
dlv.est(par, dvar, model)
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 $(T \times N)$
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 vector of the gradient. $(3N \times 1)$ for "diagonal"
and $(2N^{2}+N \times 1)$ 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 Statistics20, 339--350.
Hafner, C.M. and H. Herwartz (2008),
Analytical Quasi Maximum Likelihood Inference in Multivariate Volatility Models.Metrika67, 219--239.