Description Usage Arguments Value Note References See Also Examples
This function carries out the two step estimation of the (E)DCCGARCH model and returns estimates, standardised residuals, the estimated conditional variances, and the dynamic conditional correlations.
1 2  dcc.estimation(inia, iniA, iniB, ini.dcc, dvar, model,
method="BFGS", gradient=1, message=1)

inia 
a vector of initial values for the constants in the GARCH equation

iniA 
a matrix of initial values for the ARCH parameter matrix (N \times N) 
iniB 
a matrix of initial values for the GARCH parameter matrix (N \times N) 
ini.dcc 
a vector of initial values for the DCC parameters (2 \times 1) 
dvar 
a matrix of the data (T \times N) 
model 
a character string describing the model. 
method 
a character string specifying the optimisation method in 
gradient 
a switch variable that determines the optimisation
algorithm in the second stage optimisation. If 
message 
a switch variable to turn off the display of the message when
the estimation is completed. If 
a list with components:
out 
the parameter estimates and their standard errors 
loglik 
the value of the loglikelihood at the estimates 
h 
a matrix of the estimated conditional variances (T \times N) 
DCC 
a matrix of the estimated dynamic conditional correlations (T \times N^{2}) 
std.resid 
a matrix of the standardised residuals (T \times N). See Note. 
first 
the results of the first stage estimation 
second 
the results of the second stage estimation 
The standardised residuals are calculated by dividing the original
series dvar
by the estimated conditional standard deviations sqrt(h)
.
See Engle (2002), in particular the equations (2) and (14), for details.
The details of the first and second stage estimation are also saved in first
and second
, respectively.
The switch variable simulation
is useful when one uses dcc.estimation
for simulation.
It supresses the display of the completion message.
Engle, R.F. and K. Sheppard (2001), “Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH.” Stern Finance Working Paper Series FIN01027 (Revised in Dec. 2001), New York University Stern School of Business.
Engle, R.F. (2002), “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business and Economic Statistics 20, 339–350.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # Simulating data from the original DCCGARCH(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")
## Not run:
# Estimating a DCCGARCH(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
## End(Not run)

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