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Measuring Asset Market Linkages: Nonlinear Dependence and Tail Risk

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  • Juan Carlos Escanciano
  • Javier Hualde

Abstract

Traditional measures of dependence in time series are based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages and tail risk during abnormal times (e.g., financial contagion), they might be inappropriate. In particular, popular tail dependence measures based on exceedance correlations and marginal expected shortfall (MES) have large variances and also contain limited information on tail risk. Motivated by these limitations, we introduce the (tail-restricted) integrated regression function, and we show how it characterizes conditional dependence and persistence. We propose simple estimates for these measures and establish their asymptotic properties. We employ the proposed methods to analyze the dependence structure of some of the major international stock market indices before, during, and after the 2007–2009 financial crisis. Monte Carlo simulations and the application show that our new measures are more reliable and accurate than competing methods based on MES or exceedance correlations for testing tail dependence. Supplementary materials for this article are available online.

Suggested Citation

  • Juan Carlos Escanciano & Javier Hualde, 2021. "Measuring Asset Market Linkages: Nonlinear Dependence and Tail Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 453-465, March.
    • Handle: RePEc:taf:jnlbes:v:39:y:2021:i:2:p:453-465
    DOI: 10.1080/07350015.2019.1668797
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    More about this item

    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

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