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Dynamic tail dependence clustering of financial time series

Author

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  • Giovanni De Luca

    (University of Naples Parthenope)

  • Paola Zuccolotto

    (University of Brescia)

Abstract

In this paper we propose a dynamic clustering procedure for time series returns, aimed at providing a criterion for portfolio selection during financial crisis periods focusing attention on the lower tails of the returns distributions. In particular, for each pair of returns a time-varying distribution function is estimated using a copula function; as a result, the coefficient measuring the lower tail dependence is also time-varying with dynamics based on past market volatility. In this way we model the possible contagion between stocks when volatility increases. Accordingly, the clustering procedure based on the lower tail dependence coefficients provides different aggregations ad each time t. The clustering solutions are used to build optimal minimum Conditional Value-at-Risk portfolios able to outperform classical strategies.

Suggested Citation

  • Giovanni De Luca & Paola Zuccolotto, 2017. "Dynamic tail dependence clustering of financial time series," Statistical Papers, Springer, vol. 58(3), pages 641-657, September.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:3:d:10.1007_s00362-015-0718-7
    DOI: 10.1007/s00362-015-0718-7
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    References listed on IDEAS

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    1. Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
    2. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 323-340, December.
    3. Marco Corazza & Florence Legros & Cira Perna & Marilena Sibillo, 2017. "Mathematical and Statistical Methods for Actuarial Sciences and Finance," Post-Print hal-01776135, HAL.
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    5. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    6. Galeano, Pedro, 2001. "Multivariate analysis in vector time series," DES - Working Papers. Statistics and Econometrics. WS ws012415, Universidad Carlos III de Madrid. Departamento de Estadística.
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    Cited by:

    1. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    2. Luis Lorenzo & Javier Arroyo, 2023. "Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    3. Fuchs, Sebastian & Di Lascio, F. Marta L. & Durante, Fabrizio, 2021. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    4. Shulin Zhang & Qian M. Zhou & Huazhen Lin, 2021. "Goodness-of-fit test of copula functions for semi-parametric univariate time series models," Statistical Papers, Springer, vol. 62(4), pages 1697-1721, August.
    5. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    6. 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.
    7. Matthieu Garcin & Maxime L. D. Nicolas, 2024. "Nonparametric estimator of the tail dependence coefficient: balancing bias and variance," Statistical Papers, Springer, vol. 65(8), pages 4875-4913, October.
    8. Ji-Eun Choi & Dong Wan Shin, 2022. "Quantile correlation coefficient: a new tail dependence measure," Statistical Papers, Springer, vol. 63(4), pages 1075-1104, August.

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