An inverse-norm weighted spatial-sign max-sum test is introduced and shown, asymptotically and in simulations, to be powerful across sparse and dense alternatives for heavy-tailed high-dimensional data.
Testing Alpha in High Dimensional Linear Factor Pricing Models with Dependent Observations
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abstract
In this study, we introduce three distinct testing methods for testing alpha in high dimensional linear factor pricing model that deals with dependent data. The first method is a sum-type test procedure, which exhibits high performance when dealing with dense alternatives. The second method is a max-type test procedure, which is particularly effective for sparse alternatives. For a broader range of alternatives, we suggest a Cauchy combination test procedure. This is predicated on the asymptotic independence of the sum-type and max-type test statistics. Both simulation studies and practical data application demonstrate the effectiveness of our proposed methods when handling dependent observations.
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Inverse Norm Weighted Maxsum Test for High Dimensional Location Parameters
An inverse-norm weighted spatial-sign max-sum test is introduced and shown, asymptotically and in simulations, to be powerful across sparse and dense alternatives for heavy-tailed high-dimensional data.