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Long Memory, Realized Volatility and HAR Models

Author

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  • Richard T. Baillie

    (Michigan State University, USA, Kings College, University of London, UK & Rimini Center for Economic Analysis, Italy)

  • Fabio Calonaci

    (Queen Mary University of London)

  • Dooyeon Cho

    (Sungkyunkwan University, Republic of Korea)

  • Seunghwa Rho

    (Emory University, USA)

Abstract

The presence of long memory in Realized Volatility (RV) is a widespread stylized fact. The origins of long memory in RV have been attributed to jumps, structural breaks, non-linearities, or pure long memory. An important development has been the Heterogeneous Autoregressive (HAR) model and its extensions. This paper assesses the separate roles of fractionally integrated long memory models, extended HAR models and time varying parameter HAR models. We find that the presence of the long memory parameter is often important in addition to the HAR models.

Suggested Citation

  • Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:881
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2019/wp881.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Long memory; Restricted ARFIMA; Realized volatility; HAR model; Time varying parameters;
    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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