Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models

34 Pages Posted: 3 Nov 2008

See all articles by Robert F. Engle

Robert F. Engle

New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER); New York University (NYU) - Volatility and Risk Institute

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Date Written: January 2002

Abstract

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled 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 the likelihood function. It is shown that they perform well in a variety of situations andprovide sensible empirical results.

Suggested Citation

Engle, Robert F., Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models (January 2002). NYU Working Paper No. FIN-02-038, Available at SSRN: https://ssrn.com/abstract=1294216

Robert F. Engle (Contact Author)

New York University (NYU) - Department of Finance ( email )

Stern School of Business
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National Bureau of Economic Research (NBER) ( email )

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New York University (NYU) - Volatility and Risk Institute ( email )

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