A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
Tinbergen Institute Discussion Paper 10-032/2
32 Pages Posted: 24 Mar 2010 Last revised: 14 Oct 2010
Date Written: March 15, 2010
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.
Keywords: dynamic dependence, multivariate Student's t distribution, copula
JEL Classification: C10, C22, C32, C51
Suggested Citation: Suggested Citation