Nonlinear Least Squares Estimation of Log-ACD Models  

作　　者：Zhao CHEN Wei LIU Christina Dan WANG Wu-qing WU Yao-hua WU 

机构地区：[1]School of Data Science, Fudan University,Shanghai 200433, China [2]Academy of Mathematics and Systems Science, Chinese Academy of Sciences,Beijing 100190, China [3]New York University Shanghai, Shanghai 200122, China [4]School of Business, Renmin University of China, Beijing 100872, China [5]Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China

出　　处：《Acta Mathematicae Applicatae Sinica》2018年第3期516-533,共18页应用数学学报（英文版）

基　　金：The research was supported by the National Natural Science Foundation of China(11690014,11690015,10871188);the Research Funds of Renmin University of China(No.16XNB025);the Social Science Foundation of Beijing(No.17GLB022)

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.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.

关 键 词：Log-ACD model nonlinear least squares estimation Log-GARCH model heavy-tail 

分 类 号：O241.5[理学—计算数学]