Persistence in Variance, Structural Change, and the GARCH Model
研究股票收益数据中GARCH模型测度的方差持续性是否因忽略确定性结构变化而被高估，通过日度股票收益分析和蒙特卡洛模拟证实了这一假设。
This article examines the persistence of the variance, as measured by the generalized autoregressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate the extent to which persistence in variance may be overstated because of the existence of, and failure to take account of, deterministic structural shifts in the model. Both an analysis of daily stock-return data and a Monte Carlo simulation experiment confirm the hypothesis that GARCH measures of persistence in variance are sensitive to this type of model misspecification.