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Asymmetric dependence patterns in financial time series

Author

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  • Manuel Ammann
  • Stephan Suss

Abstract

This article proposes a new copula-based approach to test for asymmetries in the dependence structure of financial time series. Simply splitting observations into subsamples and comparing conditional correlations lead to spurious results due to the well-known conditioning bias. Our suggested framework is able to circumvent these problems. Applying our test to market data, we statistically confirm the widespread notion of significant asymmetric dependence structures between daily changes of the VIX, VXN, VDAXnew, and VSTOXX volatility indices and their corresponding equity index returns. A maximum likelihood method is used to perform a likelihood ratio test between the ordinary t-copula and its asymmetric extension. To the best of our knowledge, our study is the first empirical implementation of the skewed t-copula to generate meta-skewed Student's t-distributions. Its asymmetry leads to significant improvements in the description of the dependence structure between equity returns and implied volatility changes.

Suggested Citation

  • Manuel Ammann & Stephan Suss, 2009. "Asymmetric dependence patterns in financial time series," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 703-719.
    • Handle: RePEc:taf:eurjfi:v:15:y:2009:i:7-8:p:703-719
    DOI: 10.1080/13518470902853368
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    Citations

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    Cited by:

    1. Jin Zhang & Dietmar Maringer, 2010. "Asset Pair-Copula Selection with Downside Risk Minimization," Working Papers 037, COMISEF.
    2. Zhichao Zhang & Li Ding & Fan Zhang & Zhuang Zhang, 2015. "Optimal Currency Composition for China's Foreign Reserves: A Copula Approach," The World Economy, Wiley Blackwell, vol. 38(12), pages 1947-1965, December.
    3. Ahmed, Walid M.A., 2019. "Islamic and conventional equity markets: Two sides of the same coin, or not?," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 191-205.
    4. Aboura, Sofiane & Chevallier, Julien, 2015. "Geographical diversification with a World Volatility Index," Journal of Multinational Financial Management, Elsevier, vol. 30(C), pages 62-82.
    5. Ju, Yonghan & Jeon, Song Yi & Sohn, So Young, 2015. "Behavioral technology credit scoring model with time-dependent covariates for stress test," European Journal of Operational Research, Elsevier, vol. 242(3), pages 910-919.
    6. González, Javier & Muñoz, Alberto, 2013. "Functional analysis techniques to improve similarity matrices in discrimination problems," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 120-134.
    7. Desislava Chetalova & Marcel Wollschlager & Rudi Schafer, 2015. "Dependence structure of market states," Papers 1503.09004, arXiv.org, revised Jul 2015.
    8. Ahmed, Walid M.A., 2020. "Corruption and equity market performance: International comparative evidence," Pacific-Basin Finance Journal, Elsevier, vol. 60(C).
    9. Kenourgios, Dimitris, 2014. "On financial contagion and implied market volatility," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 21-30.
    10. Yue Peng & Wing Ng, 2012. "Analysing financial contagion and asymmetric market dependence with volatility indices via copulas," Annals of Finance, Springer, vol. 8(1), pages 49-74, February.
    11. Sarwar, Ghulam, 2022. "Market risks that change domestic diversification benefits," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).

    More about this item

    Keywords

    copulae; asymmetric dependence concepts;

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