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

Drew Creal, Siem Jan Koopman and Andre Lucas

No 10-032/2, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This discussion paper led to a publication in 'Journal of Business & Economic Statistics' , 29(4), 552-63.

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's 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. The model also admits a representation as a time-varying heavy-tailed copula which is particularly useful if the interest focuses on dependence structures. We provide an empirical illustration for a panel of daily global equity returns.

This discussion paper led to a publication in the Journal of Business and Economic Statistics (2011, 29(4) 552-63.

Keywords: dynamic dependence; multivariate Student's t distribution; copula (search for similar items in EconPapers)
JEL-codes: C10 C22 C32 C51 (search for similar items in EconPapers)
Date: 2010-03-16
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Related works:
Journal Article: A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011)
Journal Article: A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20100032

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