Nonlinear Least Squares Estimation of Log-ACD Models

被引：0
|
作者：
Zhao Chen
Wei Liu
Christina Dan Wang
Wu-qing Wu
Yao-hua Wu
机构：
[1] Fudan University,School of Data Science
[2] Chinese Academy of Sciences,Academy of Mathematics and Systems Science
[3] New York University Shanghai,School of Business
[4] Renmin University of China,Department of Statistics and Finance
[5] University of Science and Technology of China,undefined
来源：
Acta Mathematicae Applicatae Sinica, English Series | 2018年 / 34卷
关键词：
Log-ACD model; nonlinear least squares estimation; Log-GARCH model; heavy-tail; 62M10;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：516 / 533
页数：17
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