# 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

 1  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.

optim, dcc.estimation2, dcc.estimation

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