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Robust estimation of the conditional stable tail dependence function

Author

Listed:
  • Yuri Goegebeur

    (University of Southern Denmark)

  • Armelle Guillou

    (Université de Strasbourg et CNRS)

  • Jing Qin

    (University of Southern Denmark)

Abstract

We propose a robust estimator of the stable tail dependence function in the case where random covariates are recorded. Under suitable assumptions, we derive the finite-dimensional weak convergence of the estimator properly normalized. The performance of our estimator in terms of efficiency and robustness is illustrated through a simulation study. Our methodology is applied on a real dataset of sale prices of residential properties.

Suggested Citation

  • Yuri Goegebeur & Armelle Guillou & Jing Qin, 2023. "Robust estimation of the conditional stable tail dependence function," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 201-231, April.
    • Handle: RePEc:spr:aistmt:v:75:y:2023:i:2:d:10.1007_s10463-022-00839-1
    DOI: 10.1007/s10463-022-00839-1
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    References listed on IDEAS

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    1. Rosario Dell’Aquila & Paul Embrechts, 2006. "Extremes and Robustness: A Contradiction?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(1), pages 103-118, April.
    2. Beirlant, J. & Dierckx, G. & Guillou, A., 2011. "Bias-reduced estimators for bivariate tail modelling," Insurance: Mathematics and Economics, Elsevier, vol. 49(1), pages 18-26, July.
    3. Miguel de Carvalho & Manuele Leonelli & Alex Rossi, 2020. "Tracking change-points in multivariate extremes," Papers 2011.05067, arXiv.org.
    4. Fujisawa, Hironori & Eguchi, Shinto, 2008. "Robust parameter estimation with a small bias against heavy contamination," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 2053-2081, October.
    5. Gardes, Laurent & Girard, Stéphane, 2015. "Nonparametric estimation of the conditional tail copula," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 1-16.
    6. Mikael Escobar‐Bach & Yuri Goegebeur & Armelle Guillou, 2018. "Local Estimation of the Conditional Stable Tail Dependence Function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(3), pages 590-617, September.
    7. Anthony W. Ledford & Jonathan A. Tawn, 1997. "Modelling Dependence within Joint Tail Regions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 475-499.
    8. Mikael Escobar-Bach & Yuri Goegebeur & Armelle Guillou & Alexandre You, 2017. "Bias-corrected and robust estimation of the bivariate stable tail dependence function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 284-307, June.
    9. Abdelaati Daouia & Laurent Gardes & Stéphane Girard & Alexandre Lekina, 2011. "Kernel estimators of extreme level curves," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 311-333, August.
    10. Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
    11. L. Mhalla & M. de Carvalho & V. Chavez‐Demoulin, 2019. "Regression‐type models for extremal dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 46(4), pages 1141-1167, December.
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