dcc.estimation {ccgarch}R Documentation

Estimating (E)DCC-GARCH model

Description

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.

Usage

    dcc.estimation(inia, iniA, iniB, ini.dcc, dvar, model)

Arguments

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

Value

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

Note

dcc.estimation calls dcc.estimation1 and dcc.estimation2 for the first and second stage estimation respectively.

References

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.

See Also

dcc.estimation1, dcc.estimation2, loglik.dcc1, loglik.dcc2, vector.garch, dcc.est


[Package ccgarch version 0.1.1 Index]