A long-memory integer-valued time series model, INARFIMA, for financial application
Shahiduzzaman Quoreshi ()
Quantitative Finance, 2014, vol. 14, issue 12, 2225-2235
A model to account for the long-memory property in a count data framework is proposed and applied to high-frequency stock transactions data. By combining features of the INARMA and ARFIMA models, an Integer-valued Auto Regressive Fractionally Integrated Moving Average (INARFIMA) model is proposed. The unconditional and conditional first- and second-order moments are given. The CLS, FGLS and GMM estimators are discussed. In its empirical application to two stock series for AstraZeneca and Ericsson B, we find that both series have a fractional integration property.
References: Add references at CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:14:y:2014:i:12:p:2225-2235
Ordering information: This journal article can be ordered from
Access Statistics for this article
Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral
More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().