Artikel
Quasi-maximum likelihood estimation for long memory stock transaction data - under conditional heteroskedasticity framework
This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and FGLS in terms of eliminating serial correlations, but the estimator can be sensitive to start value. Hence, two-stage QML has been suggested. In empirical estimation on two stock transaction data for Ericsson and AstraZeneca, the 2SQML turns out relatively more efficient than CLS and FGLS. The empirical results suggest that both of the series have long memory properties that imply that the impact of macroeconomic news or rumors in one point of time has a persistence impact on future transactions.
- Sprache
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Englisch
- Erschienen in
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 12 ; Year: 2019 ; Issue: 2 ; Pages: 1-13 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Model Construction and Estimation
Asset Pricing; Trading Volume; Bond Interest Rates
Information and Market Efficiency; Event Studies; Insider Trading
- Thema
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count data
estimation
finance
high frequency
intraday
time series
- Ereignis
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Geistige Schöpfung
- (wer)
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Quoreshi, A. M. M. Shahiduzzaman
Uddin, Reaz
Khan, Naushad Mamode
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2019
- DOI
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doi:10.3390/jrfm12020074
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Quoreshi, A. M. M. Shahiduzzaman
- Uddin, Reaz
- Khan, Naushad Mamode
- MDPI
Entstanden
- 2019
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