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A long-memory integer-valued time series model, INARFIMA, for financial application
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
2014 (English)In: Quantitative Finance, ISSN 1469-7688, 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.

Abstract [sv]

INARFIMA modell föreslås. CLS är FGLS och GMM estimatorer diskuteras. I sin empiriska tillämpning på två aktieserier för AstraZeneca och Ericsson B finner vi att båda serierna har långminne. Intra-dag, hög frekvens, Estimation, Fractional integration, Reaktionstid

Place, publisher, year, edition, pages
Routledge , 2014. Vol. 14, no 12, 2225-2235 p.
Keyword [en]
Intra-day, High-frequency, Estimation, Fractional integration, Reaction time
National Category
Economics Probability Theory and Statistics
URN: urn:nbn:se:bth-6431DOI: 10.1080/14697688.2012.711911Local ID: diva2:833939

Available from: 2015-01-19 Created: 2015-01-13 Last updated: 2016-09-01Bibliographically approved

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Quoreshi, Shahiduzzaman
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ReferencesLink to record
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