In this study, we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely, when the number of assets p depends on the sample size n such that p{n}\to c \in (0,1) as n tends to infinity. \to c\to The considered tests are based on the sample estimator and on the shrinkage estimator of the GMVP weights. We derive the asymptotic distributions of both test statistics under the null and alternative hypotheses. Moreover, we provide a simulation study where the power functions  of the proposed tests are compared with other existing approaches. We observe that the test performs well based on the shrinkage estimator  even for values of c close to one.
In this study, we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely, when the number of assets p depends on the sample size n such that p{n}\to c \in (0,1) as n tends to infinity. In the case of a singular covariance matrix with rank equal to q we assume that q/n\to c\in(0, 1) as n\to\infty. The considered tests are based on the sample estimator and on the shrinkage estimator of the GMVP weights. We derive the asymptotic distributions of the test statistics under the null and alternative hypotheses. Moreover, we provide a simulation study where the power functions and the receiver operating characteristic curves of the proposed tests are compared with other existing approaches. We observe that the test based on the shrinkage estimator performs well even for values of c close to one.
A
In the case of a singular covariance matrix with rank equal to q we assume that q/n
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\in(0, 1) as n
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\infty.
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both
the
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and the receiver operating characteristic curves
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performs well
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performs well