ACD模型自加权LAD估计的渐近性质  

Asymptotics for the self-weighted LAD estimator of ACD models

作　　者：傅可昂 吴梦雪 黄炜[3] 王江峰[2] FU Ke-ang;WU Meng-xue;HUANG Wei;WANG Jiang-feng(Department of Statistics,Zhejiang University City College,Hangzhou 310015,China;School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China;School of Mathematical Sciences,Zhejiang University,Hangzhou 310027,China)

机构地区：[1]浙大城市学院计算机与计算科学学院,浙江杭州310015 [2]浙江工商大学统计与数学学院,浙江杭州310018 [3]浙江大学数学科学学院,浙江杭州310027

出　　处：《高校应用数学学报（A辑）》2020年第3期253-264,共12页Applied Mathematics A Journal of Chinese Universities(Ser.A)

基　　金：国家自然科学基金(11971432,11301481);浙江省一流学科A类(浙江工商大学统计学)。

摘　　要：针对高频数据建模中常用的自回归条件持续期(ACD)模型,在允许误差方差无穷的条件下,构造模型参数的自加权最小一乘(SLAD)估计,并证明了该估计的相合性和渐近正态性.数值模拟显示SLAD估计比拟极大似然估计和最小一乘估计更稳健,最后将其应用于青岛海尔和宝信软件这两只股票的价格持续期建模.The self-weighted least absolute deviation(SLAD) estimation for autoregressive conditional duration models with possibly infinite variance is proposed in this paper, and is shown to be consistent and asymptotically normal distributed. A large number of simulation studies confirm our theoretical results and suggest that the SLAD estimation is more robust compared to quasi-maximum likelihood estimation and least absolute deviation estimation. An application to two stocks’ price durations shows that the performance of SLAD estimation is more better.

关 键 词：自回归条件持续期模型 自加权最小一乘估计 相合性 渐近正态性 价格持续期 

分 类 号：O212.1[理学—概率论与数理统计]