A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations

Drew Creal, Siem Jan Koopman and Andre Lucas

Journal of Business & Economic Statistics, 2011, vol. 29, issue 4, 552-563

Abstract: 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.

Date: 2011
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Citations: View citations in EconPapers (162)

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Journal Article: A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011)
Working Paper: A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:29:y:2011:i:4:p:552-563

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DOI: 10.1198/jbes.2011.10070

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