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Asymmetry in tail dependence in equity portfolios

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  • Jondeau, Eric

Abstract

The asymmetry in the tail dependence between U.S. equity portfolios and the aggregate U.S. market is a well-established property. Given the limited number of observations in the tails of a joint distribution, standard non-parametric measures of tail dependence have poor finite-sample properties and generally reject the asymmetry in the tail dependence. A parametric model, based on a multivariate noncentral t distribution, is developed to measure and test asymmetry in tail dependence. This model allows different levels of tail dependence to be estimated depending on the distribution’s parameters and accommodates situations in which the volatilities or the correlations across returns are time varying. For most of the size, book-to-market, and momentum portfolios, the tail dependence with the market portfolio is significantly higher on the downside than on the upside.

Suggested Citation

  • Jondeau, Eric, 2016. "Asymmetry in tail dependence in equity portfolios," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 351-368.
  • Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:351-368
    DOI: 10.1016/j.csda.2015.02.014
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    9. Han Yang & Ming-hui Wang & Nan-jing Huang, 2021. "The $$\alpha$$ α -Tail Distance with an Application to Portfolio Optimization Under Different Market Conditions," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1195-1224, December.
    10. Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
    11. Caporale, Guglielmo Maria & You, Kefei & Chen, Lei, 2019. "Global and regional stock market integration in Asia: A panel convergence approach," International Review of Financial Analysis, Elsevier, vol. 65(C).
    12. Carsten Bormann & Melanie Schienle, 2020. "Detecting Structural Differences in Tail Dependence of Financial Time Series," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 380-392, April.
    13. Broda, Simon A. & Krause, Jochen & Paolella, Marc S., 2018. "Approximating expected shortfall for heavy-tailed distributions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 184-203.
    14. Lei Jiang & Esfandiar Maasoumi & Jiening Pan & Ke Wu, 2018. "A test of general asymmetric dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 1026-1043, November.
    15. Arnold Polanski & Evarist Stoja & Ching‐Wai (Jeremy) Chiu, 2021. "Tail risk interdependence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5499-5511, October.

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