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Tail Dependence in Financial Markets: A Dynamic Copula Approach

Author

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  • Federico Pasquale Cortese

    (University of Milano—Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy)

Abstract

This article is concerned with the study of the tail correlation among equity indices by means of dynamic copula functions. The main idea is to consider the impact of the use of copula functions in the accuracy of the model’s parameters and in the computation of Value-at-Risk (VaR). Results show that copulas provide more sophisticated results in terms of the accuracy of the forecasted VaR, in particular, if they are compared with the results obtained from Dynamic Conditional Correlation (DCC) model.

Suggested Citation

  • Federico Pasquale Cortese, 2019. "Tail Dependence in Financial Markets: A Dynamic Copula Approach," Risks, MDPI, vol. 7(4), pages 1-14, November.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:4:p:116-:d:285787
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    References listed on IDEAS

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    Cited by:

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    2. Giuseppe Arbia & Riccardo Bramante & Silvia Facchinetti, 2020. "Least Quartic Regression Criterion to Evaluate Systematic Risk in the Presence of Co-Skewness and Co-Kurtosis," Risks, MDPI, vol. 8(3), pages 1-14, September.
    3. Paravee Maneejuk & Woraphon Yamaka, 2021. "The Role of Economic Contagion in the Inward Investment of Emerging Economies: The Dynamic Conditional Copula Approach," Mathematics, MDPI, vol. 9(20), pages 1-23, October.

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