Publication Type

Journal Article

Version

Postprint

Publication Date

7-2002

Abstract

In this article we propose a new multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model with time-varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. Whereas each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analog. Our new model retains the intuition and interpretation of the univariate GARCH model and yet satisfies the positive-definite condition as found in the constant-correlation and Baba-Engle-Kraft-Kroner models We report some Monte Carlo results on the finite-sample distributions of the maximum likelihood estimate of the varying-correlation MGARCH model. The new model is applied to some real data sets.

Keywords

BEKK model, Constant correlation, Maximum likelihood estimate, Monte Carlo method, Multivariate GARCH model, Varying correlation

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Business and Economic Statistics

Volume

20

Issue

3

First Page

351

Last Page

362

ISSN

0735-0015

Identifier

10.1198/073500102288618496

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1198/073500102288618496

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Econometrics Commons

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