Robust High Dimensional Alpha Test for Linear Factor Pricing Model
针对高维线性因子定价模型，提出基于空间符号的极大值型检验来检测稀疏备择，并构造柯西组合检验，对厚尾分布稳健且在不同稀疏水平下均有较高检验功效。
ABSTRACT In this paper, we investigate alpha testing for high‐dimensional linear factor pricing models. We propose a spatial‐sign‐based max‐type test to detect sparse alternatives. Additionally, the asymptotic independence between this test and the existing spatial‐sign‐based sum‐type test is established. Based on this result, we introduce a Cauchy combination test procedure that combines both the max‐type and sum‐type tests. Simulation studies and real data applications demonstrate that the proposed test procedure is robust to heavy‐tailed distributions and powerful against alternatives with different sparsity levels.