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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Year of publication:	2006-04-11
Authors:	Quoreshi, Shahiduzzaman
Institutions:	Institutionen för Nationalekonomi, Umeå Universitet
Subject:	Intra-day \| High frequency \| Estimation \| Fractional integration \| Reaction time

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Extent:	application/pdf
Series:	Umeå Economic Studies.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series Umeå Economic Studies Number 673 19 pages
Classification:	C13 - Estimation ; C22 - Time-Series Models ; C25 - Discrete Regression and Qualitative Choice Models ; C51 - Model Construction and Estimation ; G12 - Asset Pricing ; G14 - Information and Market Efficiency; Event Studies
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005198001