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

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Author Info

  • 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.

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File URL: ftp://ftp.econ.au.dk/creates/rp/12/rp12_30.pdf
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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2012-30.

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Length: 42
Date of creation: 12 Jun 2012
Date of revision:
Handle: RePEc:aah:create:2012-30

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Smooth transitions; long memory; forecasting; realized volatility.;

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