Adaptive Testing for Alphas in High-Dimensional Factor Pricing Models
提出一种检验多因子定价理论的新方法，针对大量资产中少数异常资产存在的稀疏信号，基于高维高斯近似理论模拟检验的极限零分布，相比现有方法在稀疏备择下显著提升检验功效。
This article proposes a new procedure to validate the multi-factor pricing theory by testing the presence of alpha in linear factor pricing models with a large number of assets. Because the market’s inefficient pricing is likely to occur to a small fraction of exceptional assets, we develop a testing procedure that is particularly powerful against sparse signals. Based on the high-dimensional Gaussian approximation theory, we propose a simulation-based approach to approximate the limiting null distribution of the test. Our numerical studies show that the new procedure can deliver a reasonable size and achieve substantial power improvement compared to the existing tests under sparse alternatives, and especially for weak signals.