• Medientyp: E-Book
  • Titel: Testing High-Dimensional Covariance Matrices Under the Elliptical Distribution and Beyond
  • Beteiligte: Yang, Xinxin [Verfasser:in]; Zheng, Xinghua [Sonstige Person, Familie und Körperschaft]; Chen, Jiaqi [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [S.l.]: SSRN, [2020]
  • Umfang: 1 Online-Ressource (31 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.3001811
  • Identifikator:
  • Entstehung:
  • Anmerkungen: In: Journal of Econometrics, Forthcoming
    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 13, 2017 erstellt
  • Beschreibung: 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
  • Zugangsstatus: Freier Zugang