This function computes various analytical derivatives of the second stage loglikelihood function (the DCC part) of the (E)DCCGARCH model.
1  dlc(dcc.para, B, u, h, model)

dcc.para 
the estimates of the (E)DCC parameters (2 \times 1) 
B 
the estimated GARCH parameter matrix (N \times N) 
u 
a matrix of the used for estimating the (E)DCCGARCH model (T \times N) 
h 
a matrix of the estimated conditional variances (T \times N) 
model 
a character string describing the model. 
a list with components:
dlc 
the gradient of the DCC loglikelihood function w.r.t. the DCC parameters (T \times 2) 
dvecP 
the partial derivatives of the DCC matrix, P_t w.r.t. the DCC parameters (T \times N^{2}) 
dvecQ 
the partial derivatives of the Q_t matrices w.r.t. the DCC parameters (T \times N^{2}) 
d2lc 
the Hessian of the DCC loglikelihood function w.r.t. the DCC parameters (T \times 4) 
dfdwd2lc 
the cross derivatives of the DCC loglikelihood function (T \times npar.h+2)
npar.h stand for the number of parameters in the GARCH part, npar.h = 3N
for 
Engle, R.F. and K. Sheppard (2001), “Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH.” Stern Finance Working Paper Series FIN01027 (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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