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Statistical Inference for High-Dimensional Global Minimum Variance Portfolios

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  • Konstantin Glombek

Abstract

type="main" xml:id="sjos12066-abs-0001"> Many studies demonstrate that inference for the parameters arising in portfolio optimization often fails. The recent literature shows that this phenomenon is mainly due to a high-dimensional asset universe. Typically, such a universe refers to the asymptotics that the sample size n + 1 and the sample dimension d both go to infinity while d ∕ n → c ∈ (0,1). In this paper, we analyze the estimators for the excess returns’ mean and variance, the weights and the Sharpe ratio of the global minimum variance portfolio under these asymptotics concerning consistency and asymptotic distribution. Problems for stating hypotheses in high dimension are also discussed. The applicability of the results is demonstrated by an empirical study. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Konstantin Glombek, 2014. "Statistical Inference for High-Dimensional Global Minimum Variance Portfolios," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 845-865, December.
    • Handle: RePEc:bla:scjsta:v:41:y:2014:i:4:p:845-865
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    File URL: http://hdl.handle.net/10.1111/sjos.12066
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    References listed on IDEAS

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    Cited by:

    1. Taras Bodnar & Stepan Mazur & Krzysztof Podgórski, 2017. "A test for the global minimum variance portfolio for small sample and singular covariance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 253-265, July.
    2. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    3. Muhinyuza, Stanislas & Bodnar, Taras & Lindholm, Mathias, 2020. "A test on the location of the tangency portfolio on the set of feasible portfolios," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    4. Philip L.H. Yu & Thomas Mathew & Yuanyuan Zhu, 2017. "A generalized pivotal quantity approach to portfolio selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(8), pages 1402-1420, June.
    5. Bodnar, Taras & Parolya, Nestor & Thorsén, Erik, 2023. "Is the empirical out-of-sample variance an informative risk measure for the high-dimensional portfolios?," Finance Research Letters, Elsevier, vol. 54(C).

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