Testing High-Dimensional Covariance Matrices Under the Elliptical Distribution and Beyond
31 Pages Posted: 19 Jul 2017 Last revised: 15 Jun 2020
Date Written: July 13, 2017
We develop tests for high-dimensional covariance matrices under a generalized elliptical model. Our tests are based on a central limit theorem for linear spectral statistics of the sample covariance matrix based on self-normalized observations. For testing sphericity, our tests neither assume specific parametric distributions nor involve the kurtosis of data. More generally, we can test against any non-negative definite matrix that can even be not invertible. As an interesting application, we illustrate in empirical studies that our tests can be used to test uncorrelatedness among idiosyncratic returns.
Keywords: covariance matrix, high-dimension, elliptical model, linear spectral statistics, central limit theorem, self-normalization
JEL Classification: C12, C55
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