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A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations
Open accessAbstract
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.
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Cited by
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Chiang, T. C., Jeon, B. N., & Li, H. (2007) · Journal of International Money and Finance · doi:10.1016/j.jimonfin.2007.06.005 · Cited by 894
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Silvennoinen, A., & Thorp, S. (2013) · Journal of International Financial Markets, Institutions and Money · doi:10.1016/j.intfin.2012.11.007 · Cited by 650
Silvennoinen, A., & Teräsvirta, T. (2009) · Handbook of Financial Time Series · doi:10.1007/978-3-540-71297-8_9 · Cited by 452
TEMPORAL AGGREGATION OF UNIVARIATE AND MULTIVARIATE TIME SERIES MODELS: A SURVEY
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Metadata
- Authors
- Tse, Y. K., & Tsui, A. K. C.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2002-07
- Volume
- 20
- Issue
- 3
- Pages
- 351-362
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2079898209
- Cited by
- 1,197
- References
- 0
- FWCI
- 27.32
- Type
- journal article
- Language
- en
- Open access
- green
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- Tse, Y. K., & Tsui, A. K. C. (2002). A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations. Journal of Business & Economic Statistics. https://doi.org/10.1198/073500102288618496
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