Nonlinear Least Squares Estimation of Log-ACD Models

Zhao Chen; Wei Liu; Christina Dan Wang; Wu qing Wu; Yao hua Wu

doi:10.1007/s10255-018-0766-6

Nonlinear Least Squares Estimation of Log-ACD Models

Zhao Chen, Wei Liu, Christina Dan Wang, Wu qing Wu, Yao hua Wu

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2 Scopus citations

Abstract

This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.

Original language	English (US)
Pages (from-to)	516-533
Number of pages	18
Journal	Acta Mathematicae Applicatae Sinica
Volume	34
Issue number	3
DOIs	https://doi.org/10.1007/s10255-018-0766-6
State	Published - Jul 1 2018

All Science Journal Classification (ASJC) codes

Applied Mathematics

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title = "Nonlinear Least Squares Estimation of Log-ACD Models",

abstract = "This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.",

author = "Zhao Chen and Wei Liu and Wang, {Christina Dan} and Wu, {Wu qing} and Wu, {Yao hua}",

note = "Publisher Copyright: {\textcopyright} 2018, Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature.",

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AU - Chen, Zhao

AU - Liu, Wei

AU - Wang, Christina Dan

AU - Wu, Wu qing

AU - Wu, Yao hua

PY - 2018/7/1

Y1 - 2018/7/1

N2 - This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.

AB - This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.

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Nonlinear Least Squares Estimation of Log-ACD Models

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