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A long-memory integer-valued time series model, INARFIMA, for financial application
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för industriell ekonomi.
2014 (engelsk)Inngår i: Quantitative Finance, ISSN 1469-7688, Vol. 14, nr 12, s. 2225-2235Artikkel i tidsskrift (Fagfellevurdert) 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

sted, utgiver, år, opplag, sider
Routledge , 2014. Vol. 14, nr 12, s. 2225-2235
Emneord [en]
Intra-day, High-frequency, Estimation, Fractional integration, Reaction time
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-6431DOI: 10.1080/14697688.2012.711911Lokal ID: oai:bth.se:forskinfo3C918ECD41431757C1257DCC007700C3OAI: oai:DiVA.org:bth-6431DiVA, id: diva2:833939
Merknad

http://www.tandfonline.com/doi/full/10.1080/14697688.2012.711911#.VLWQdivF9oc

Tilgjengelig fra: 2015-01-19 Laget: 2015-01-13 Sist oppdatert: 2016-09-01bibliografisk kontrollert

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