Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2002
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
Citation
TSE, Yiu Kuen and TSUI, Albert K.C..
A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations. (2002). Journal of Business and Economic Statistics. 20, (3), 351-362.
Available at: https://ink.library.smu.edu.sg/soe_research/348
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1198/073500102288618496