# dcc_results: Computing robust standard errors of the estimates in the... In ccgarch: Conditional Correlation GARCH models

## Description

This function computes the robust standard errors of the estimates of a DCC-GARCH model.

## Usage

 1 dcc.results(u, garch.para, dcc.para, h, model)

## Arguments

 u a matrix of the data used for estimating the (E)DCC-GARCH model (T \times N) garch.para a vector of the estimates of the volatility parameters dcc.para a vector of the estimates of the DCC parameters (2 \times 1) h a matrix of the estimated conditional variances (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 matrix with the estimates in the first row, and the standard errors in the second row.

## Note

dcc.results is called from dcc.estimation. When model="diagonal", only the diagonal entries in A and B are used.

## 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.