Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH

43 Pages Posted: 3 Nov 2008

See all articles by Robert F. Engle

Robert F. Engle

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

Kevin Sheppard

University of Oxford - Department of Economics; University of Oxford - Oxford-Man Institute of Quantitative Finance

Multiple version iconThere are 3 versions of this paper

Date Written: November 2001

Abstract

In this paper, we develop the theoretical and empirical properties of a new class of multivariate 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 Indicesand Dow Jones Industrial Average stocks, and conduct specification tests of the estimatorusing an industry standard benchmark for volatility models. This new estimator demonstrates very strong performance especially considering ease of implementation of the estimator.

Suggested Citation

Engle, Robert F. and Sheppard, Kevin Keith, Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH (November 2001). NYU Working Paper No. FIN-01-027, Available at SSRN: https://ssrn.com/abstract=1294564

Robert F. Engle (Contact Author)

New York University (NYU) - Department of Finance

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Kevin Keith Sheppard

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