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Testing for Asymmetric Comovements

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  • O‐Chia Chuang
  • Xiaojun Song
  • Abderrahim Taamouti

Abstract

This paper aims to provide non‐parametric tests for asymmetric comovements between random variables. We consider the popular Cramér‐von Mises and Kolmogorov–Smirnov test statistics based on the distance between positive and negative joint conditional exceedance distribution functions. These tests can capture both linear and nonlinear dependence in the data and do not require selecting kernel functions and bandwidths. We derive the asymptotic distributions of the tests and establish the validity of a block multiplier‐type bootstrap that one can use in finite‐sample settings. We also show that these tests are consistent for any fixed alternative and have non‐trivial power for detecting local alternatives converging to the null at the parametric rate. Monte Carlo simulations and a real financial data analysis illustrate satisfactory performance of the proposed tests.

Suggested Citation

  • O‐Chia Chuang & Xiaojun Song & Abderrahim Taamouti, 2022. "Testing for Asymmetric Comovements," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1153-1180, October.
    • Handle: RePEc:bla:obuest:v:84:y:2022:i:5:p:1153-1180
    DOI: 10.1111/obes.12485
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    References listed on IDEAS

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    Cited by:

    1. Linyu Cao & Ruili Sun & Tiefeng Ma & Conan Liu, 2023. "On Asymmetric Correlations and Their Applications in Financial Markets," JRFM, MDPI, vol. 16(3), pages 1-18, March.

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