dcc_estimation2: Maximising the second stage log-likelihood function of the...

In ccgarch: Conditional Correlation GARCH models

Description

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

Usage

    dcc.estimation2(dvar, para, gradient=0)

Arguments

dvar	a matrix of the standardised residuals (T \times N)
para	a vector of the DCC parameters (2 \times 1)
gradient	a switch variable whether to use the gradient in the constraint optimisation. passed to constrOptim

Value

a list of the estimation results. See the explanations for constrOptim.

Note

dcc.estimation2 is a wrapper to constrOptim. The restrictions are α + β ≤q 1 and α, β ≥q 0 in the DCC equation.

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

constrOptim, dcc.estimation1, dcc.estimation

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