ccgarch (version 0.2.3)

loglik.dcc: The log-likelihood function for the (E)DCC GARCH model

Description

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

Usage

loglik.dcc(param, dvar, model)

Arguments

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

Value

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

References

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.

Examples

## 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)