LongMemory, Count Data, Time Series Modelling for Financial Application
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.
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