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Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework

1
Department of Industrial Economics, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden
2
Department of Economics and Statistics, Faculty of Social Sciences and Humanities, University of Mauritius, Reduit 80837, Mauritius
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2019, 12(2), 74; https://doi.org/10.3390/jrfm12020074
Received: 15 March 2019 / Revised: 17 April 2019 / Accepted: 25 April 2019 / Published: 27 April 2019
(This article belongs to the Special Issue Analysis of Global Financial Markets)
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Abstract

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. View Full-Text
Keywords: count data; estimation; finance; high frequency; intraday; time series count data; estimation; finance; high frequency; intraday; time series
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Quoreshi, A.M.M.S.; Uddin, R.; Mamode Khan, N. Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework. J. Risk Financial Manag. 2019, 12, 74.

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