A spatial-sign-based multiple testing framework (SS-BH and FSS-BH) that controls the false discovery rate when selecting skilled mutual funds under heavy-tailed errors and latent factors.
Double Robust high dimensional alpha test for linear factor pricing model
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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 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.
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Robust Mutual Fund Selection with False Discovery Rate Control
A spatial-sign-based multiple testing framework (SS-BH and FSS-BH) that controls the false discovery rate when selecting skilled mutual funds under heavy-tailed errors and latent factors.