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Regression‐type models for extremal dependence

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  • L. Mhalla
  • M. de Carvalho
  • V. Chavez‐Demoulin

Abstract

We propose a vector generalized additive modeling framework for taking into account the effect of covariates on angular density functions in a multivariate extreme value context. The proposed methods are tailored for settings where the dependence between extreme values may change according to covariates. We devise a maximum penalized log‐likelihood estimator, discuss details of the estimation procedure, and derive its consistency and asymptotic normality. The simulation study suggests that the proposed methods perform well in a wealth of simulation scenarios by accurately recovering the true covariate‐adjusted angular density. Our empirical analysis reveals relevant dynamics of the dependence between extreme air temperatures in two alpine resorts during the winter season.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:scjsta:v:46:y:2019:i:4:p:1141-1167
    DOI: 10.1111/sjos.12388
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    Cited by:

    1. Linda Mhalla & Julien Hambuckers & Marie Lambert, 2022. "Extremal connectedness of hedge funds," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 988-1009, August.
    2. 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.
    3. Yang, Lu & Hamori, Shigeyuki, 2023. "Modeling the global sovereign credit network under climate change," International Review of Financial Analysis, Elsevier, vol. 87(C).

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