非对称Log-GARCH模型及重尾新息下的准极大似然估计  

Asymmetric Log-GARCH Model and Maximum Likelihood Estimator Based on Heavy-Tailed Errors

作　　者：董晨昱[1] 刘维奇[2] 

机构地区：[1]山西大学数学科学学院,山西太原030006 [2]山西大学管理科学与工程研究所,山西太原030006

出　　处：《山西大学学报（自然科学版）》2009年第3期349-352,共4页Journal of Shanxi University(Natural Science Edition)

基　　金：山西省人文社会科学重点研究基地项目"金融复杂性实证研究"(20083006)

摘　　要：在Geweke(1986)和Pantula(1986)提出的Log-GARCH模型及Nelson(1991)提出的EGARCH模型的基础上,提出了非对称Log-GARCH(Asymmetric Log-GARCH)模型,该模型不仅考虑了信息非对称效应,和EGARCH模型相比,在重尾误差下更容易得到准极大似然估计,最后文章还给出了估计的渐进性质.In the present paper, Asymmetric Log-GARCH Model was put forward on the basis of Log- GARCH, rasied by Geweke（1986） and Pantula（1986）, and the EGARCH Model, rasied by Nelson （1991）. This Model not only took Asymmetric information effect into account, and at the same time, compared with the EGARCH Model,but also was easier to obtain the Quasimaximum likelihood estimator in the case of Heavy-tailed errors. At last, the asymptotic nature of the estimator was provided.

关 键 词：非对称 Log-GARCH 重尾误差 准极大似然估计 

分 类 号：O211[理学—概率论与数理统计]