Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH
Robert F. Engle
New York University - Leonard N. Stern School of Business - Department of Economics; National Bureau of Economic Research (NBER); New York University (NYU) - Department of Finance
University of Oxford - Department of Economics; University of Oxford - Oxford-Man Institute of Quantitative Finance
NYU Working Paper No. FIN-01-027
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.
Number of Pages in PDF File: 43working papers series
Date posted: November 3, 2008
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