Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH

44 Pages Posted: 18 Oct 2001 Last revised: 17 Nov 2022

See all articles by Kevin Sheppard

Kevin Sheppard

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

Robert F. Engle

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

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Date Written: October 2001

Abstract

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.

Suggested Citation

Sheppard, Kevin Keith and Engle, Robert F., Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH (October 2001). NBER Working Paper No. w8554, Available at SSRN: https://ssrn.com/abstract=287751

Kevin Keith Sheppard

University of Oxford - Department of Economics ( email )

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Robert F. Engle (Contact Author)

New York University (NYU) - Department of Finance ( email )

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