Abstract

http://ssrn.com/abstract=1296428
 
 

References (13)



 
 

Citations (192)



 


 



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; New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)

January 2002

NYU Working Paper No. S-DRP-02-01

Abstract:     
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 andprovide sensible empirical results.

Number of Pages in PDF File: 34

working papers series


Download This Paper

Date posted: November 7, 2008  

Suggested Citation

Engle, Robert F., Dynamic Conditional Correlation : A Simple Class of Multivariate GARCH Models (January 2002). NYU Working Paper No. S-DRP-02-01. Available at SSRN: http://ssrn.com/abstract=1296428

Contact Information

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
Feedback to SSRN


Paper statistics
Abstract Views: 3,697
Downloads: 1,172
Download Rank: 1,338
References:  13
Citations:  192

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo3 in 0.516 seconds