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A bivariate integer-valued long-memory model for high-frequency financial count data
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för industriell ekonomi.
2017 (engelsk)Inngår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 46, nr 3, s. 1080-1089Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Taylor & Francis, 2017. Vol. 46, nr 3, s. 1080-1089
Emneord [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
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Identifikatorer
URN: urn:nbn:se:bth-13482DOI: 10.1080/03610926.2014.997361ISI: 000387274200004Scopus ID: 2-s2.0-84994034965OAI: oai:DiVA.org:bth-13482DiVA, id: diva2:1049430
Tilgjengelig fra: 2016-11-24 Laget: 2016-11-23 Sist oppdatert: 2017-11-29bibliografisk kontrollert

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