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Long Memory Conditional Heteroscedasticity in Count Data

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  • 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
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    File URL: https://www.wiwi.uni-muenster.de/cqe/sites/cqe/files/CQE_Paper/cqe_wp_82_2019.pdf
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    References listed on IDEAS

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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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    Keywords

    Count Data; Poisson Autoregression; Fractionally Integrated; INGARCH;
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