Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86860
Authors: 
Creal, Drew
Koopman, Siem Jan
Lucas, André
Year of Publication: 
2010
Series/Report no.: 
Tinbergen Institute Discussion Paper 10-032/2
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'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.
Subjects: 
dynamic dependence
multivariate Student's t distribution
copula
JEL: 
C10
C22
C32
C51
Document Type: 
Working Paper

Files in This Item:
File
Size
528.48 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.