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Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models

UCSD Economics Discussion Paper No. 2000-09

28 Pages Posted: 1 Dec 2000  

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)

Multiple version iconThere are 4 versions of this paper

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 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 and give sensible empirical results.

Keywords: ARCH, GARCH, Correlation, Time Series, Value at Risk

JEL Classification: C1

Suggested Citation

Engle, Robert F., Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models (May 2000). UCSD Economics Discussion Paper No. 2000-09. Available at SSRN: https://ssrn.com/abstract=236998 or http://dx.doi.org/10.2139/ssrn.236998

Robert F. Engle (Contact Author)

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

269 Mercer Street
New York, NY 10003
United States

New York University (NYU) - Department of Finance

Stern School of Business
44 West 4th Street
New York, NY 10012-1126
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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