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Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models

Author

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  • Eric Hillebrand

    (Aarhus University and CREATES)

  • Marcelo C. Medeiros

    (PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO)

Abstract

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.

Suggested Citation

  • Eric Hillebrand & Marcelo C. Medeiros, 2012. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," CREATES Research Papers 2012-30, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2012-30
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    References listed on IDEAS

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    More about this item

    Keywords

    Smooth transitions; long memory; forecasting; realized volatility.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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