Chen Zhao1; Liu Wei2; Wang Christina Dan3; Wu Wuqing4; Wu Yaohua5
Source Publicationactamathematicaeapplicataesinica
AbstractThis 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.
Document Type期刊论文
Affiliation1.School of Data Science,Fudan University
4.School of Business,Renmin University of China
5.Department of Statistics and Finance,University of Science and Technology of China
Recommended Citation
GB/T 7714
Chen Zhao,Liu Wei,Wang Christina Dan,et al. nonlinearleastsquaresestimationoflogacdmodels[J]. actamathematicaeapplicataesinica,2018,034(003):516.
APA Chen Zhao,Liu Wei,Wang Christina Dan,Wu Wuqing,&Wu Yaohua.(2018).nonlinearleastsquaresestimationoflogacdmodels.actamathematicaeapplicataesinica,034(003),516.
MLA Chen Zhao,et al."nonlinearleastsquaresestimationoflogacdmodels".actamathematicaeapplicataesinica 034.003(2018):516.
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