英文文献：Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models
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.