A linear varying coefficient ARCH-M model with a latent variable  

作　　者：SONG ZeFang ZHANG XingFa LI Yuan XIONG Qiang 

机构地区：[1]School of Economics and Statistics, Guangzhou University [2]Lingnan Research Center for Statistical Science, Guangzhou University

出　　处：《Science China Mathematics》2016年第9期1795-1814,共20页中国科学：数学（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 11271095 and 11401123);the Doctoral Program of Higher Education of China (Grant No. 20124410110002)

摘　　要：Motivated by the psychological factor of time-varying risk-return relationship, this paper studies a linear varying coefficient ARCH-M model with a latent variable. Due to the unobservable property of the latent variable, a corrected likelihood method is employed for parametric estimation. Estimators are proved to be consistent and asymptotically normal under certain regularity conditions. A simple test statistic is also proposed for testing latent variable effect. Simulation results confirm that the proposed estimators and test perform well.The model is further applied to examine whether the risk-return relationship depends on investor's sentiment in American Market and some explainable results are obtained.Motivated by the psychological factor of time-varying risk-return relationship, this paper studies a linear varying coefficient ARCH-M model with a latent variable. Due to the unobservable property of the latent variable, a corrected likelihood method is employed for parametric estimation. Estimators are proved to be consistent and asymptotically normal under certain regularity conditions. A simple test statistic is also proposed for testing latent variable effect. Simulation results confirm that the proposed estimators and test perform well.The model is further applied to examine whether the risk-return relationship depends on investor＇s sentiment in American Market and some explainable results are obtained.

关 键 词：ARCH-M model latent variable corrected likelihood risk-return relationship 

分 类 号：O212.1[理学—概率论与数理统计]