英文文献：Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models-时间序列模型中的非线性、间断和长期依赖
英文文献作者：Eric Hillebrand,Marcelo C. Medeiros
英文文献摘要：
We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building procedures are proposed. 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.

我们研究了在ARMA时间序列模型中同时出现的长记忆和非线性效应，如参数变化和阈值效应，并将我们的建模框架应用于每日实现的波动率。提出了参数估计的渐近理论，并提出了两种模型的建立方法。该方法适用于2000年至2009年期间道琼斯工业平均指数的股票。我们发现非线性效应的有力证据。