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Download the Paper (253 K, PDF file) - May 1, 2000 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

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.

SUGGESTED CITATION:
Robert F. Engle, "Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models" (May 1, 2000). Department of Economics, UCSD. Paper 2000-09.
http://repositories.cdlib.org/ucsdecon/2000-09