Time‐Series Factor Modeling and Selection
提出一种包含确定性正交趋势多项式的统计时间序列因子模型，用于捕捉收益变化、测试和选择相关因子，并识别资产定价模型中是否遗漏因子，通过Fama-French五因子模型和因子动物园的实证应用展示效果。
Abstract The article proposes a statistical time‐series factor model that incorporates deterministic orthogonal trend polynomials. Such polynomials allow capturing variation in returns without initially identifying a set of robust time‐series factors. This modeling approach can serve as a coherent basis for testing and selecting the most relevant factors among a set of possible ones. Additionally, it can help identify whether any factors are missing from a time‐series asset pricing model. The use of the proposed model and empirical strategy is illustrated by two empirical applications from the literature, yielding results related to the Fama‐French five‐factor model and the factor zoo.