Double Robust high dimensional alpha test for linear factor pricing model

Fuente: arXiv
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Auteurs principaux: Zhao, Ping, Feng, Long, Wang, Hongfei, Wang, Zhaojun
Format: Preprint
Publié: 2024
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author Zhao, Ping
Feng, Long
Wang, Hongfei
Wang, Zhaojun
author_facet Zhao, Ping
Feng, Long
Wang, Hongfei
Wang, Zhaojun
contents In this paper, we investigate alpha testing for high-dimensional linear factor pricing models. We propose a spatial sign-based max-type test to handle sparse alternative cases. Additionally, we prove that this test is asymptotically independent of the spatial-sign-based sum-type test proposed by Liu et al. (2023). 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 new proposed test procedure is robust not only for heavy-tailed distributions but also for the sparsity of the alternative hypothesis.
format Preprint
id arxiv_https___arxiv_org_abs_2408_06612
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Double Robust high dimensional alpha test for linear factor pricing model
Zhao, Ping
Feng, Long
Wang, Hongfei
Wang, Zhaojun
Methodology
In this paper, we investigate alpha testing for high-dimensional linear factor pricing models. We propose a spatial sign-based max-type test to handle sparse alternative cases. Additionally, we prove that this test is asymptotically independent of the spatial-sign-based sum-type test proposed by Liu et al. (2023). 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 new proposed test procedure is robust not only for heavy-tailed distributions but also for the sparsity of the alternative hypothesis.
title Double Robust high dimensional alpha test for linear factor pricing model
topic Methodology
url https://arxiv.org/abs/2408.06612