LongMemory, Count Data, Time Series Modelling for Financial Application

A.M.M. 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)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
Date: 2006-04-11
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