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Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility

Author

Listed:
  • Richard T. Baillie

    (Michigan State University
    University of London)

  • Dooyeon Cho

    (Sungkyunkwan University)

  • Seunghwa Rho

    (Hanyang University)

Abstract

Several articles have attempted to approximate long-memory, fractionally integrated time series by fitting a low-order autoregressive AR(p) model and making subsequent inference. We show that for realistic ranges of the long-memory parameter, the OLS estimates of an AR(p) model will have non-standard rates of convergence to non-standard distributions. This gives rise to very poorly estimated AR parameters and impulse response functions. We consider the implications of this in some AR type models used to represent realized volatility (RV) in financial markets.

Suggested Citation

  • Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2024. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Advanced Studies in Theoretical and Applied Econometrics,, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-48385-1_17
    DOI: 10.1007/978-3-031-48385-1_17
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    More about this item

    Keywords

    Long-memory; ARFIMA; Realized volatility; HAR models;
    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
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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