Dynamic Conditional Correlation a Simple Class of Multivariate GARCH Models
Robert F. Engle
New York University - Leonard N. Stern School of Business - Department of Economics; National Bureau of Economic Research (NBER); New York University (NYU) - Department of Finance
NYU Working Paper No. FIN-00-034
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 coupled1 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 thelikelihood function. It is shown that they perform well in a variety of situationsand give sensible empirical results.
Number of Pages in PDF File: 27working papers series
Date posted: November 4, 2008
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo6 in 0.484 seconds