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April 27, 2026Oxford Bulletin of Economics and Statistics0 citations

Robust High Dimensional Alpha Test for Linear Factor Pricing Model

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PZPing ZhaoLFLong FengHWHongfei Wang

Key Points

  • This research aims to develop and validate a robust alpha testing procedure for high-dimensional linear factor pricing models.
  • Proposed a spatial-sign-based max-type test to detect sparse alternatives.
  • Established asymptotic independence between the max-type and existing sum-type tests.
  • Introduced a Cauchy combination test procedure combining both max-type and sum-type tests.
  • Demonstrated robustness of the proposed test to heavy-tailed distributions.
  • Showed high power against alternatives with varying sparsity levels in simulation studies.
  • Applied methods effectively to real data, confirming applicability and strength.

Abstract

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.

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Cite This Study

Zhao et al. (2026) studied this question.

synapsesocial.com/papers/69eefde9fede9185760d4addhttps://doi.org/10.1111/obes.70080
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