Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH

Robert F. Engle and Kevin Sheppard ()

No 8554, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C32 G0 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2001-10
Note: AP
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Working Paper: Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH (2001)
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