Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915936308559872 |
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| author | Zhao, Ping Wang, Hongfei |
| author_facet | Zhao, Ping Wang, Hongfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12252 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models Zhao, Ping Wang, Hongfei Methodology 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. |
| title | Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models |
| topic | Methodology |
| url | https://arxiv.org/abs/2604.12252 |