摘要
探讨了因子模型中特征值的波动特点,并提出了一种新的模型结构检验方法。根据特征值的特点,通过随机化方法将未知分布的变量转化为已知分布的统计量。检验统计量检查包括因子载荷的变化和因子数量的增加两种因子模型结构的中断。研究给出模拟实验的结果,并对2017年1月1日至2019年12月31日中美股市股票收益数据的因子结构进行检验。所提方法在仿真和实际数据中均表现良好。
We consider the fluctuation of eigenvalues in factor models and propose a new method for testing the model.Based on the characteristics of eigenvalues,variables of unknown distribution are transformed into statistics of known distribution through randomization.The test statistic checks for breaks in the structure of factor models,including changes in factor loadings and increases in the number of factors.We give the results of simulation experiments and test the factor structure of the stock return data of China’s and U.S.stock markets from January 1,2017,to December 31,2019.Our method performs well in both simulations and real data.
作者
鲍方琳
张博
Fanglin Bao;Bo Zhang(Department of Statistics and Finance,International Institute of Finance,School of Management,University of Science and Technology of China,Hefei 230026,China)
出处
《中国科学技术大学学报》
CAS
CSCD
北大核心
2023年第11期53-61,I0006,I0008,共11页
JUSTC
基金
supported by the National Natural Science Foundation of China(12001517,72091212)
the USTC Research Funds of the Double First-Class Initiative(YD2040002005)
the Fundamental Research Funds for the Central Universities(WK2040000026,WK2040000027)。
关键词
因子模型
特征值波动
高维数据
随机矩阵理论
factor models
eigenvalue fluctuation
high-dimensional data
random matrix theory
