The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
Open accessAbstract
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations and volatilities. When we account for heavy tails of distributions, we obtain estimates that are more robust to large innovations. We provide an empirical illustration for a panel of daily equity returns.
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Cites
Generalized autoregressive conditional heteroskedasticity
Bollerslev, T. (1986) · Journal of Econometrics · doi:10.1016/0304-4076(86)90063-1 · Cited by 22,254
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Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model
Bollerslev, T. (1990) · The Review of Economics and Statistics · doi:10.2307/2109358 · Cited by 3,512
A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return
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Metadata
- Authors
- Creal, D., Koopman, S. J., & Lucas, A.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2011-10
- Volume
- 29
- Issue
- 4
- Pages
- 552-563
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2153947535
- Cited by
- 293
- References
- 28
- FWCI
- 32.04
- Type
- journal article
- Language
- en
- Open access
- green
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- Creal, D., Koopman, S. J., & Lucas, A. (2011). A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations. Journal of Business & Economic Statistics. https://doi.org/10.1198/jbes.2011.10070
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