ccgarch (version 0.2.2)

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

# 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")