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A long-memory integer-valued time series model, INARFIMA, for financial application
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
2014 (English)In: Quantitative finance (Print), ISSN 1469-7688, E-ISSN 1469-7696, Vol. 14, no 12, 2225-2235 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis, 2014. Vol. 14, no 12, 2225-2235 p.
Keyword [en]
High-frequency, Intra-day, Reaction time, Fractional integration, Estimation, C51 G12, C25, G14, C22, C13
National Category
Economics
Research subject
Economics
Identifiers
URN: urn:nbn:se:umu:diva-58343DOI: 10.1080/14697688.2012.711911ISI: 000344592300014OAI: oai:DiVA.org:umu-58343DiVA: diva2:547939
Available from: 2012-08-29 Created: 2012-08-29 Last updated: 2017-12-07Bibliographically approved

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Quoreshi, A. M. M. Shahiduzzaman
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