Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models

UCSD Economics Discussion Paper No. 2000-09

28 Pages Posted: 1 Dec 2000

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

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 (NYU) - Department of Finance

Stern School of Business
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New York, NY 10012-1126
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
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New York University (NYU) - Volatility and Risk Institute ( email )

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New York, NY 10012
United States

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