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Inference on the maximal rank of time-varying covariance matrices using high-frequency data

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  • Reiß, Markus
  • Winkelmann, Lars

Abstract

We study the rank of the instantaneous or spot covariance matrix ΣX(t) of a multidimensional continuous semi-martingale X(t). Given highfrequency observations X(i/n), i = 0,...,n, we test the null hypothesis rank (ΣX(t))

Suggested Citation

  • Reiß, Markus & Winkelmann, Lars, 2021. "Inference on the maximal rank of time-varying covariance matrices using high-frequency data," Discussion Papers 2021/14, Free University Berlin, School of Business & Economics.
    • Handle: RePEc:zbw:fubsbe:202114
    DOI: 10.17169/refubium-32210
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    References listed on IDEAS

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    7. Yacine Aït-Sahalia & Dacheng Xiu, 2019. "Principal Component Analysis of High-Frequency Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 287-303, January.
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    Cited by:

    1. Andrea Bucci & Michele Palma & Chao Zhang, 2024. "Geometric Deep Learning for Realized Covariance Matrix Forecasting," Papers 2412.09517, arXiv.org.

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    More about this item

    Keywords

    empirical covariance matrix; rank detection; signal detection rate; matrix concentration; eigenvalue perturbation; principal component analysis; factor model; term structure;
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