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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

May 2000

UCSD Economics Discussion Paper No. 2000-09

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.

Number of Pages in PDF File: 28

Keywords: ARCH, GARCH, Correlation, Time Series, Value at Risk

JEL Classification: C1

working papers series


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Date posted: December 1, 2000  

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: http://ssrn.com/abstract=236998 or http://dx.doi.org/10.2139/ssrn.236998

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
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
New York University (NYU) - Department of Finance
Stern School of Business
44 West 4th Street
New York, NY 10012-1126
United States
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