Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models
研究了时间序列模型中长记忆性与非线性效应（如参数变化和阈值效应）的同时发生，将模型应用于日度已实现波动率，发现金融波动率中存在强非线性效应，且建模这些效应能提升预测表现。
We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance. Supplementary materials for this article are available online.