loglik.dcc {ccgarch} | R Documentation |
This function returns a log-likelihood of the (E)DCC-GARCH model.
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.
# 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")