Abstract

http://ssrn.com/abstract=1293628
 
 

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High Dimension Dynamic Correlations


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)

August 2007

NYU Working Paper No. FIN-07-045

Abstract:     
This paper develops time series methods for forecasting correlations in high dimensional problems. The Dynamic Conditional Correlation model is given a new convenient estimation approach called the MacGyver method. It is compared with the FACTOR ARCH model and a new model called the FACTOR DOUBLE ARCH model. Finally the latter model is blended with the DCC to give a FACTOR DCC model. This family of models is estimated with daily returns from 18 US large cap stocks. Economic loss functions designed to form optimal portfolios and optimal hedges are used to compare the performance of the methods. The best approach invariably is the FACTOR DCC and the next best is the FACTOR DOUBLE ARCH.

Number of Pages in PDF File: 45

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Date posted: November 3, 2008  

Suggested Citation

Engle, Robert F., High Dimension Dynamic Correlations (August 2007). NYU Working Paper No. FIN-07-045. Available at SSRN: http://ssrn.com/abstract=1293628

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
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