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Long Memory, Realized Volatility and Heterogeneous Autoregressive Models

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  • Richard T. Baillie
  • Fabio Calonaci
  • Dooyeon Cho
  • Seunghwa Rho

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, contemporaneous aggregation, nonlinearities, or pure long memory. An important development has been the heterogeneous autoregressive (HAR) model and its extensions. This article 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.

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  • Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and Heterogeneous Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(4), pages 609-628, July.
  • Handle: RePEc:bla:jtsera:v:40:y:2019:i:4:p:609-628
    DOI: 10.1111/jtsa.12470
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    File URL: https://doi.org/10.1111/jtsa.12470
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    Cited by:

    1. Emmanuel Joel Aikins Abakah & Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Economic Policy Uncertainty: Persistence and Cross-Country Linkages," CESifo Working Paper Series 8289, CESifo.
    2. Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.
    3. Constandina Koki & Loukia Meligkotsidou & Ioannis Vrontos, 2020. "Forecasting under model uncertainty: Non‐homogeneous hidden Markov models with Pòlya‐Gamma data augmentation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 580-598, July.

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