Glasnik Matematicki, Vol. 60, No. 2 (2025), 353-373. \( \)

PERMUTATION TEST OF INDEPENDENCE IN TAILS FOR DEPENDENT PROCESSES

Darko Brborović

Faculty of Informatics, University of Pula, Pula, Croatia
e-mail:darko.brborovic1@gmail.com


Abstract.   In this article, we propose a permutation test for independence in the tails of two strongly mixing and strictly stationary sequences. We establish the asymptotic validity of the test by demonstrating that both the test statistic and its permutation distribution are asymptotically normal. These results build upon and generalize findings from Basrak and Brborović [1]. Additionally, we conduct a simulation study to evaluate the size and power properties of the proposed test.

2020 Mathematics Subject Classification.   60F05, 62G09, 62G32, 62E20

Key words and phrases.   Permutation test, Central limit theorem, Independence in tails, Extreme value analysis


Full text (PDF) (access from subscribing institutions only)

https://doi.org/10.3336/gm.60.2.09


References:

  1. B. Basrak and D. Brborović, Permutation test of tail dependence, Stat. Methods Appl. 33 (2024), 89–129.
    MathSciNet    CrossRef

  2. D. Brborović, Statistical analysis of the tail behaviour of dependent sequences, PhD thesis, University of Zagreb, Faculty of Science, Department of Mathematics, 2022.

  3. P. Billingsley, Probability and measure, John Wiley & Sons, Inc., New York, 1995.
    MathSciNet

  4. R. C. Bradley, Introduction to strong mixing conditions, Kendrick Press, Heber City, 2009.
    MathSciNet

  5. E. Chung and J. P. Romano, Exact and asymptotically robust permutation tests, Ann. Statist 41 (2013), 484–507.
    MathSciNet    CrossRef

  6. R. Cont, Empirical properties of asset returns: stylized facts and statistical issues, Quantitative Finance 1 (2001), 223–236.
    CrossRef

  7. V. H. de la Pena, R. Ibragimov and S. Sharakhmetov, Characterizations of joint distributions, copulas, information, dependence and decoupling, with applications to time series, Institute of Mathematical Statistics, Beachwood, 2006, 183–209.
    MathSciNet    CrossRef

  8. C. J. DiCiccio and J. P. Romano Robust permutation tests for correlation and regression coefficients, J. Amer. Statist. Assoc. 112 (2017), 1211–1220.
    MathSciNet    CrossRef

  9. P. Embrechts, A. McNeil and D. Straumann Correlation and dependence in risk management: properties and pitfalls, In Risk Management: Value at Risk and Beyond. Cambridge University Press, Cambridge, 2002, 176–223.
    CrossRef

  10. A. Galanos, rugarch: Univariate GARCH models. R package version 1.5-3., 2024.

  11. V. Hoeffding, A combinatorial central limit theorem, Ann. Math. Statistics 22 (1951), 558–566.
    MathSciNet    CrossRef

  12. M. Hofert, I. Kojadinovic, M. Maechler and J. Yan, copula: Multivariate dependence with copulas. R package version 1.1-6, 2024.
    Link

  13. M. Hofert and Ma. Maechler, Nested Archimedean copulas meet R: The nacopula package. Journal of Statistical Software 39 (2011), 1-20.
    Link

  14. I. A. Ibragimov, Some limit theorems for stationary processes, Teor. Verojatnost. i Primenen. 7 (1962), 361–392.
    MathSciNet

  15. O. Kallenberg, Foundations of modern probability, Springer-Verlag, New York, 2002.
    MathSciNet    CrossRef

  16. I. Kojadinovic and J. Yan, Modeling multivariate distributions with continuous margins using the copula R package, Journal of Statistical Software 34 (2010), 1–20. .

  17. E. L. Lehman, and J. P. Romano, Testing statistical hypotheses, Springer, New York, 2005.
    MathSciNet

  18. A. M. Lindner, Stationarity, mixing, distributional properties and moments of GARCH(p, q)–processes. In Handbook of financial time series, Springer, Berlin, Heidelberg, 2009.
    CrossRef

  19. R Core Team (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

  20. S. I. Resnick, Heavy tail phenomena, probabilistic and statistical modeling, Springer New York, 2007.
    CrossRef

  21. J. P. Romano and M. A. Tirlea, Permutation testing for dependence in time series, J. Time Series Anal. 43 (2022), 781–807.
    MathSciNet    CrossRef

  22. G. Simpson, permute: Functions for generating restricted permutations of data. R package version 0.9-7, 2022.
    Link

  23. M. A. Tirlea, Permutation-based inference in time series analysis, PhD Thesis, Stanford University, 2023.

  24. J. Yan, Enjoy the joy of copulas: with a package copula, Journal of Statistical Software 21 (2007), 1–21.
    Link

Glasnik Matematicki Home Page