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

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Author Info

  • Drew Creal

    (University of Chicago, Booth School of Business)

  • Siem Jan Koopman

    (VU University Amsterdam)

  • Andr� Lucas

    (VU University Amsterdam)

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.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 10-032/2.

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Date of creation: 16 Mar 2010
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Handle: RePEc:dgr:uvatin:20100032

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Web page: http://www.tinbergen.nl

Related research

Keywords: dynamic dependence; multivariate Student's t distribution; copula;

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