TY  - JOUR
AU  - Engle,Robert F.
AU  - Sheppard,Kevin
TI  - Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 8554
PY  - 2001
Y2  - October 2001
UR  - http://www.nber.org/papers/w8554
L1  - http://www.nber.org/papers/w8554.pdf
N1  - Author contact info:
Robert F. Engle, III
Department of Finance, Stern School of Business
New York University, Salomon Center
44 West 4th Street, Suite 9-160
New York, NY  10012-1126
Tel: 212/998-0710
Fax: 212/995-4220
E-Mail: rengle@stern.nyu.edu
AB  - In this paper, we develop the theoretical and empirical properties of a new class of multi-variate GARCH models capable of estimating large time-varying covariance matrices, Dynamic Conditional Correlation Multivariate GARCH. We show that the problem of multivariate conditional variance estimation can be simplified by estimating univariate GARCH models for each asset, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. The standard errors for the first stage parameters remain consistent, and only the standard errors for the correlation parameters need be modified. We use the model to estimate the conditional covariance of up to 100 assets using S&P 500 Sector Indices and Dow Jones Industrial Average stocks, and conduct specification tests of the estimator using an industry standard benchmark for volatility models. This new estimator demonstrates very strong performance especially considering ease of implementation of the estimator.
ER  -