dcc_estimation1: Maximising the first stage log-likelihood function of the...

In ccgarch: Conditional Correlation GARCH models

Description

This function carries out the first stage (volatility part) estimation of the (E)DCC-GARCH model.

Usage

    dcc.estimation1(dvar, a, A, B, model, method="BFGS")

Arguments

dvar	a matrix of the data used for estimating the (E)DCC-GARCH(1,1) model (T \times N)
a	a vector of constants in the vector GARCH equation (N \times 1)
A	an ARCH parameter matrix in the vector GARCH equation (N \times N)
B	a GARCH parameter matrix in the vector GARCH equation (N \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
method	a character string specifying the optimisation method in optim. There are three choices, namely, "Nelder-Mead", "BFGS" (default) and "CG".

Value

a list of the estimation results. See the explanations in optim.

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

optim, dcc.estimation2, dcc.estimation

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