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A test of asymmetric comovement for state-dependent stock returns

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  • Deng, Kaihua

Abstract

I propose a test of asymmetric stock return comovement across states. The test can be viewed as a variation of Kendall's τ conditional on the state and has an asymptotic χ2-distribution. A refined version of the test is derived based on the Markov chain theory of regenerative cycles which substantially improves finite sample size and power properties. I show that the test has power against local alternatives, which is nonetheless compromised due to a finite sample convergence bound put on the implied local alternative data generating process. I evaluate the new test against traditional correlation-based measures and demonstrate power attrition of a state-free tail dependence test as parameter values are varied. Broad market-based ETFs and international indices are studied and in most cases there is no compelling evidence for asymmetric comovement across states. A list of related tests is given as an extension at the end.

Suggested Citation

  • Deng, Kaihua, 2016. "A test of asymmetric comovement for state-dependent stock returns," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 68-85.
    • Handle: RePEc:eee:empfin:v:36:y:2016:i:c:p:68-85
    DOI: 10.1016/j.jempfin.2016.01.009
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    Cited by:

    1. Chung Baek, 2020. "Risk Transmissions between Major Foreign Currencies: An Empirical Analysis from the U.S. Perspective," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 19(2), pages 151-168, September.

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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