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A bivariate integer-valued long-memory model for high-frequency financial count data
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
2017 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 46, no 3, 1080-1089 p.Article in journal (Refereed) Published
Abstract [en]

We propose a bivariate integer-valued fractional integrated (BINFIMA) model to account for the long-memory property and apply the model to high-frequency stock transaction data. The BINFIMA model allows for both positive and negative correlations between the counts. The unconditional and conditional first- and second-order moments are given. The model is capable of capturing the covariance between and within intra-day time series of high-frequency transaction data due to macroeconomic news and news related to a specific stock. Empirically, it is found that Ericsson B has mean recursive process while AstraZeneca has long-memory property.

Place, publisher, year, edition, pages
Taylor & Francis, 2017. Vol. 46, no 3, 1080-1089 p.
Keyword [en]
Count data, Estimation, Finance, Intra-day, Reaction time, Time series, Human reaction time, Count datum, High frequency HF, Long-memory property, Negative correlation, Recursive process, Second order moment, Stock transaction, Bins
National Category
Business Administration
Identifiers
URN: urn:nbn:se:bth-13482DOI: 10.1080/03610926.2014.997361ISI: 000387274200004ScopusID: 2-s2.0-84994034965OAI: oai:DiVA.org:bth-13482DiVA: diva2:1049430
Available from: 2016-11-24 Created: 2016-11-23 Last updated: 2016-12-05Bibliographically approved

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Quoreshi, Shahiduzzaman
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CiteExportLink to record
Permanent link

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Citation style
  • apa
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  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf