dcc.estimation {ccgarch} | R Documentation |
This function carries out the two step estimation of the (E)DCC-GARCH model and returns estimates, standardised residuals, the estimated volatility, and the dynamic conditional correlation. The details of the first and second stage estimation are also reported.
dcc.estimation(inia, iniA, iniB, ini.dcc, dvar, model)
inia |
a vector of initial values for the constants in the GARCH equation (N times 1) |
iniA |
a matrix of initial values for the ARCH parameter (N times N) |
iniB |
a matrix of initial values for the GARCH parameter (N times N) |
ini.dcc |
a vector of initial values for the DCC parameters (2 times 1) |
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 |
a list with components:
out |
the estimates and their standard errors |
h |
a matrix of the estimated volatilities (T times N) |
DCC |
a matrix of DCC estimates (T times N^{2}) |
first |
the results of the first stage estimation |
second |
the results of the second stage estimation |
dcc.estimation
calls dcc.estimation1
and dcc.estimation2
for the
first and second stage estimation respectively.
Engle, R.F. and K. 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.
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.
dcc.estimation1
,
dcc.estimation2
,
loglik.dcc1
,
loglik.dcc2
,
vector.garch
,
dcc.est