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

Author

Listed:
  • 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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    Cited by:

    1. Pedro Chaim & Márcio Poletti Laurini, 2024. "Bayesian Inference for Long Memory Stochastic Volatility Models," Econometrics, MDPI, vol. 12(4), pages 1-28, November.
    2. Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," Econometrics, MDPI, vol. 6(2), pages 1-25, April.
    3. Segnon Mawuli & Lau Chi Keung & Wilfling Bernd & Gupta Rangan, 2022. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(1), pages 73-98, February.
    4. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    5. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.

    More about this item

    Keywords

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    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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