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 observed residuals $(T \times N)$
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 Statistics20, 339--350.# 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")ts,
models,
multivariate