A case study on the shareholder network effect of stock market data:An SARMA approach  

作　　者：Rong Zhang Jing Zhou Wei Lan Hansheng Wang 

机构地区：[1]School of Mathematics and Statistics,Yunnan University,Kunming 650500,China [2]Center for Applied Statistics,School of Statistics,Renmin University of China,Beijing 100872,China [3]The Center of Statistical Research,Southwestern University of Finance and Economics,Chengdu 611130,China [4]Guanghua School of Management,Peking University,Beijing 100871,China

出　　处：《Science China Mathematics》2022年第11期2219-2242,共24页中国科学：数学（英文版）

基　　金：supported by the Major Program of the National Natural Science Foundation of China (Grant No. 11731101);National Natural Science Foundation of China (Grant No. 11671349);supported by National Natural Science Foundation of China (Grant No. 72171226);the Beijing Municipal Social Science Foundation (Grant No. 19GLC052);the National Statistical Science Research Project (Grant No. 2020LZ38);supported by National Natural Science Foundation of China (Grant Nos. 71532001, 11931014, 12171395 and 71991472);the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economics;supported by National Natural Science Foundation of China (Grant No. 11831008);the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (Grant No. Klatasds-Moe-EcnuKlatasds2101)

摘　　要：One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.

关 键 词：spatial autoregressive moving average model shareholder network effect quasi-maximum likelihood estimator stock market data 

分 类 号：F832.51[经济管理—金融学] O212.1[理学—概率论与数理统计]