LongMemory, Count Data, Time Series Modelling for Financial Application
Shahiduzzaman Quoreshi
No 673, Umeå Economic Studies from Umeå University, Department of Economics
Abstract:
A model to account for the long memory property in a count data framework
is proposed and applied to high frequency stock transactions data.
The unconditional and conditional first and second order moments are
given. The CLS and FGLS 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.
Keywords: Intra-day; High frequency; Estimation; Fractional integration; Reaction time (search for similar items in EconPapers)
JEL-codes: C13 C22 C25 C51 G12 G14 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2006-04-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:umnees:0673
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