Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks

This paper concerns the properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Proofs of consistency and asymptotic normality of QMLE for the Log-ACD model with log-normal density are presented. This is an important issue a...

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Main Authors: Allen, D., Chan, Felix, McAleer, M., Peiris, S.
Format: Journal Article
Published: Elsevier 2008
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/34878
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author Allen, D.
Chan, Felix
McAleer, M.
Peiris, S.
author_facet Allen, D.
Chan, Felix
McAleer, M.
Peiris, S.
author_sort Allen, D.
building Curtin Institutional Repository
collection Online Access
description This paper concerns the properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Proofs of consistency and asymptotic normality of QMLE for the Log-ACD model with log-normal density are presented. This is an important issue as the Log-ACD is used widely for testing various market microstructure models and effects. Knowledge of the distribution of the QMLE is crucial for purposes of valid inference and diagnostic checking. The theoretical results developed in the paper are evaluated using Monte Carlo experiments. The experimental results also provide insights into the finite sample properties of the Log-ACD model under different distributional assumptions. Finally, this paper presents two extensions to the Log-ACD model to accommodate asymmetric effects. The usefulness of these novel models will be evaluated empirically using data from Australian stocks.
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institution Curtin University Malaysia
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publishDate 2008
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spelling curtin-20.500.11937-348782017-09-13T16:08:47Z Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks Allen, D. Chan, Felix McAleer, M. Peiris, S. Monte Carlo simulation Conditional Duration ACD Lof-ACD Asymmetry This paper concerns the properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Proofs of consistency and asymptotic normality of QMLE for the Log-ACD model with log-normal density are presented. This is an important issue as the Log-ACD is used widely for testing various market microstructure models and effects. Knowledge of the distribution of the QMLE is crucial for purposes of valid inference and diagnostic checking. The theoretical results developed in the paper are evaluated using Monte Carlo experiments. The experimental results also provide insights into the finite sample properties of the Log-ACD model under different distributional assumptions. Finally, this paper presents two extensions to the Log-ACD model to accommodate asymmetric effects. The usefulness of these novel models will be evaluated empirically using data from Australian stocks. 2008 Journal Article http://hdl.handle.net/20.500.11937/34878 10.1016/j.jeconom.2008.09.020 Elsevier fulltext
spellingShingle Monte Carlo simulation
Conditional Duration
ACD
Lof-ACD
Asymmetry
Allen, D.
Chan, Felix
McAleer, M.
Peiris, S.
Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks
title Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks
title_full Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks
title_fullStr Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks
title_full_unstemmed Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks
title_short Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks
title_sort finite sample properties of the qmle for the log-acd model: application to australian stocks
topic Monte Carlo simulation
Conditional Duration
ACD
Lof-ACD
Asymmetry
url http://hdl.handle.net/20.500.11937/34878