Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH / Robert F. Engle, Kevin Sheppard.

Format:

Book

Author/Creator:

Engle, Robert F.

Contributor:

National Bureau of Economic Research.

Sheppard, Kevin.

Series:

Working Paper Series (National Bureau of Economic Research) no. w8554.

NBER working paper series no. w8554

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2001.

Summary:

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.

Notes:

Print version record

October 2001.