loglik_dcc: The log-likelihood function for the (E)DCC GARCH model
In ccgarch: Conditional Correlation GARCH models

Description Usage Arguments Value Note References Examples

This function returns a log-likelihood of the (E)DCC-GARCH model.

1	loglik.dcc(param, dvar, model)

`param`	a vector of all the parameters in the (E)DCC-GARCH model
`dvar`	a matrix of the data used for estimating the (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

the negative of the full log-likelihood of the (E)DCC-GARCH model

param must be made by stacking all the parameter matrices.

Robert F. Engle and Kevin 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.

Robert F. Engle (2002), “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalised Autoregressive Conditional Heteroskedasticity Models.” Journal of Business and Economic Statistics 20, 339–350.

## Not run: 
# Simulating data from the original DCC-GARCH(1,1) process
  nobs <- 1000; cut <- 1000
  a <- c(0.003, 0.005, 0.001)
  A <- diag(c(0.2,0.3,0.15))
  B <- diag(c(0.75, 0.6, 0.8))
  uncR <- matrix(c(1.0, 0.4, 0.3, 0.4, 1.0, 0.12, 0.3, 0.12, 1.0),3,3)
  dcc.para <- c(0.01,0.98)
  dcc.data <- dcc.sim(nobs, a, A, B, uncR, dcc.para, model="diagonal")

# Estimating a DCC-GARCH(1,1) model
  dcc.results <- dcc.estimation(inia=a, iniA=A, iniB=B, ini.dcc=dcc.para, 
    dvar=dcc.data$eps, model="diagonal")

# Parameter estimates and their robust standard errors
  dcc.results$out

# Computing the value of the log-likelihood at the estimates
  loglik.dcc(dcc.results$out[1,], dcc.data$eps, model="diagonal")

## End(Not run)

****************************************************************
*  Estimation has been completed.                              *
*  The outputs are saved in a list with components:            *
*    out    : the estimates and their standard errors          *
*    loglik : the value of the log-likelihood at the estimates *
*    h      : a matrix of estimated conditional variances      *
*    DCC    : a matrix of DCC estimates                        *
*    std.resid : a matrix of the standardised residuals        *
*    first  : the results of the first stage estimation        *
*    second : the results of the second stage estimation       *
****************************************************************
                   a1          a2           a3         A11        A22
estimates 0.004682004 0.005913054 0.0008216825 0.224032419 0.29364617
std.err   0.001376104 0.040764186 0.0588748334 0.001384191 0.04445823
                 A33          B11        B22        B33   dcc alpha   dcc beta
estimates 0.16778953 0.6727981270 0.59307039 0.78997454 0.013401120 0.97677524
std.err   0.05138622 0.0003028481 0.02992004 0.03873222 0.004549742 0.01016453
[1] 6569.34