Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models
Abstract
This paper develops a new framework for alpha testing in high-dimensional factor pricing models with time-varying coefficients. To detect sparse alternatives, we propose a spatial-sign-based max-type test and derive its limiting null distribution. A key theoretical result is that our statistic is asymptotically independent of the spatial-sign-based sum-type test proposed by Zhao (2023). Exploiting this independence, we construct an adaptive testing procedure via the Cauchy combination method. This approach integrates the complementary strengths of both max-type and sum-type statistics, ensuring robust power across diverse sparsity levels. Extensive simulations and an empirical application demonstrate that the proposed test is resilient to heavy-tailed distributions and maintains superior performance under various alternative specifications.
Keywords
Cite
@article{arxiv.2604.12252,
title = {Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models},
author = {Ping Zhao and Hongfei Wang},
journal= {arXiv preprint arXiv:2604.12252},
year = {2026}
}