Adaptive Testing for Alphas in Conditional Factor Models with High Dimensional Assets
针对时间变化因子定价模型中的阿尔法检验问题，提出了一种在备择假设稀疏时表现良好的最大值型检验，并与和型检验结合构建自适应检验，通过模拟和实际数据验证其优势。
This article focuses on testing for the presence of alpha in time-varying factor pricing models, specifically when the number of securities <i>N</i> is larger than the time dimension of the return series <i>T</i>. We introduce a maximum-type test that performs well in scenarios where the alternative hypothesis is sparse. We establish the limit null distribution of the proposed maximum-type test statistic and demonstrate its asymptotic independence from the sum-type test statistics proposed by Ma et al. Additionally, we propose an adaptive test by combining the maximum-type test and sum-type test, and we show its advantages under various alternative hypotheses through simulation studies and two real data applications.