Eric Hillebrand

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

Publikation: Working paperForskning

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  • Rp12 30

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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.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider42
StatusUdgivet - 2 jul. 2012
SerietitelCREATES Research Papers
Nummer2012-30

    Forskningsområder

  • Smooth transitions, long memory, forecasting, realized volatility

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