A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
提出一类新的观测驱动时变参数模型，用于处理厚尾分布时间序列的动态波动率和相关性，通过广义自回归得分动态估计多元t分布的时变协方差矩阵，并对滞后平方创新进行加权以提高估计稳健性。
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.