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Tail index varying coefficient model

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  • Yaolan Ma
  • Yuexiang Jiang
  • Wei Huang

Abstract

This paper deals with a new class of tail index varying coefficient models with the random covariate under Pareto-type distributions. To estimate the unknown coefficient functions, we develop an estimation procedure via a local polynomial maximum likelihood techniques. The asymptotic normality of the estimated coefficient functions under some mild regularity conditions are established. Two numerical examples and one application are used to illustrate the performance of the proposed procedure.

Suggested Citation

  • Yaolan Ma & Yuexiang Jiang & Wei Huang, 2019. "Tail index varying coefficient model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(2), pages 235-256, January.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:2:p:235-256
    DOI: 10.1080/03610926.2017.1406519
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    Cited by:

    1. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
    2. Koki Momoki & Takuma Yoshida, 2024. "Hypothesis testing for varying coefficient models in tail index regression," Statistical Papers, Springer, vol. 65(6), pages 3821-3852, August.
    3. João Nicolau & Pedro Raposo & Paulo M. M. Rodrigues, 2023. "Measuring wage inequality under right censoring," Economic Inquiry, Western Economic Association International, vol. 61(2), pages 377-401, April.
    4. Hongyu An & Boping Tian, 2024. "Varying Index Coefficient Model for Tail Index Regression," Mathematics, MDPI, vol. 12(13), pages 1-34, June.

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