A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations

D.D. Creal, S.J. Koopman, A. Lucas

Research output: Contribution to JournalArticleAcademicpeer-review

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. © 2011 American Statistical Association.
Original languageEnglish
Pages (from-to)552-563
JournalJournal of Business and Economic Statistics
Volume29
Issue number4
DOIs
Publication statusPublished - 2011

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