Dynamic Conditional Correlation – a Simple Class of Multivariate Garch Models

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 and provide sensible empirical results. 1 This research has been supported by NSF grant SBR-9730062 and NBER AP group. The author wishes to thank Kevin Sheppard for research assistance, and Pat Burns and John Geweke for insightful comments. Thanks also go to seminar participants at New York University, UCSD, Academica Sinica, Taiwan, CNRS Montreal, University of Iowa, Journal of Applied Econometrics Lectures, Cambridge, England, CNRS Aussois, Brown University, Fields Institute University of Toronto, and Riskmetrics.