Long Memory Conditional Heteroscedasticity in Count Data
Segnon Mawuli, Stapper Manuel
Abstract
This paper introduces a new class of integer-valued long memory processesthat are adaptations of the well-known FIGARCH(p, d, q) process of Baillie (1996) andHYGARCH(p, d, q) process of Davidson (2004) to a count data setting. We derive thestatistical properties of the models and show that reasonable parameter estimates areeasily obtained via conditional maximum likelihood estimation. An empirical application with financial transaction data illustrates the practical importance of the models.
Keywords
Count Data; Poisson Autoregression; Fractionally Integrated; INGARCH
Publication type
Other scientific publication
Publication status
Published
Year
2019
Volume
82/2019
Title of series
CQE-Working-Papers
Publisher
Center for Quantitative Economics (CQE), University of Muenster
Place
University of Muenster
Language
English
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