loglik_dcc: The log-likelihood function for the (E)DCC GARCH model

Description Usage Arguments Value Note References Examples

Description

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

Usage

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   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

Note

param must be made by stacking all the parameter matrices.

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

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## 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)

ccgarch documentation built on May 29, 2017, 12:58 p.m.