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Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule

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  • Woźny, Jakub
  • Jaworski, Piotr
  • Jelito, Damian
  • Pitera, Marcin
  • Wyłomańska, Agnieszka

Abstract

We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing. The proposed test clusters the data according to the 20/60/20 rule and confronts conditional covariance (or correlation) estimates on the obtained subsets. The corresponding test statistic has a natural practical interpretation, desirable statistical properties, and asymptotic pivotal distribution under the multivariate normality assumption. We illustrate the usefulness of the introduced framework using extensive power simulation studies and show that our approach outperforms popular benchmark alternatives. Also, we apply the proposed methodology to exemplary commodity and equity market data.

Suggested Citation

  • Woźny, Jakub & Jaworski, Piotr & Jelito, Damian & Pitera, Marcin & Wyłomańska, Agnieszka, 2025. "Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule," Journal of Multivariate Analysis, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:jmvana:v:206:y:2025:i:c:s0047259x24001039
    DOI: 10.1016/j.jmva.2024.105396
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    References listed on IDEAS

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