Dynamic Conditional Correlation a Simple Class of Multivariate GARCH Models

27 Pages Posted: 4 Nov 2008

See all articles by Robert F. Engle

Robert F. Engle

New York University - Leonard N. Stern School of Business - Department of Economics; New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)

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Date Written: May 2000

Abstract

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of returns. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled1 with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on thelikelihood function. It is shown that they perform well in a variety of situationsand give sensible empirical results.

Suggested Citation

Engle, Robert F., Dynamic Conditional Correlation a Simple Class of Multivariate GARCH Models (May 2000). NYU Working Paper No. FIN-00-034. Available at SSRN: https://ssrn.com/abstract=1295238

Robert F. Engle (Contact Author)

New York University - Leonard N. Stern School of Business - Department of Economics ( email )

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New York University (NYU) - Department of Finance

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