Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models
Eric Hillebrand () and
Marcelo Medeiros ()
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
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.
Keywords: Smooth transitions; long memory; forecasting; realized volatility. (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Journal Article: Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2012-30
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