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Long Memory Conditional Heteroscedasticity in Count Data

Author

Listed:
  • Mawuli Segnon
  • Manuel Stapper
Abstract
This paper introduces a new class of integer-valued long memory processes that are adaptations of the well-known FIGARCH(p, d, q) process of Baillie (1996) and HYGARCH(p, d, q) process of Davidson (2004) to a count data setting. We derive the statistical properties of the models and show that reasonable parameter estimates are easily obtained via conditional maximum likelihood estimation. An empirical application with financial transaction data illustrates the practical importance of the models.

Suggested Citation

  • Mawuli Segnon & Manuel Stapper, 2019. "Long Memory Conditional Heteroscedasticity in Count Data," CQE Working Papers 8219, Center for Quantitative Economics (CQE), University of Muenster.
    • Handle: RePEc:cqe:wpaper:8219
    as

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    File URL: https://www.wiwi.uni-muenster.de/cqe/sites/cqe/files/CQE_Paper/cqe_wp_82_2019.pdf
    File Function: Version of May 2019
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    References listed on IDEAS

    as
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    7. A. M. M. Shahiduzzaman Quoreshi, 2014. "A long-memory integer-valued time series model, INARFIMA, for financial application," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2225-2235, December.
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    12. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
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    Cited by:

    1. Mawuli Segnon, 2022. "Strict stationarity of Poisson integer-valued ARCH processes of order infinity," CQE Working Papers 10222, Center for Quantitative Economics (CQE), University of Muenster.

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    More about this item

    Keywords

    Count Data; Poisson Autoregression; Fractionally Integrated; INGARCH;
    All these keywords.

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