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Application of one-factor copula with Durante generators to high-dimensional data: empirical study on stock market of China

Journal of physics. Conference series, 2021-07, Vol.1978 (1), p.12045 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1978/1/012045

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  • Title:
    Application of one-factor copula with Durante generators to high-dimensional data: empirical study on stock market of China
  • Author: Cheng, Yangnan ; Liu, Jianxu ; Sriboonchitta, Songsak
  • Subjects: COVID-19 ; Generators ; Securities markets
  • Is Part Of: Journal of physics. Conference series, 2021-07, Vol.1978 (1), p.12045
  • Description: Abstract This paper investigates the performance of one-Factor copula with Durante generators (FDG copula) in high-dimensional applications. We use data with 28, 102 and 227 dimensions respectively to compare the mean absolute error in three cases. The results show that estimation error deceases as dimension increases, which means the higher the dimension, the better this model performs. Empirically, we measure the dependence between industrial sectors in Chinese stock market by FDG copulas. It is found that Machinery and equipment sector has the largest dependence coefficients with other sectors. In addition, by comparing the results before and during the COVID-19 pandemic, we find that the epidemic strengthened the connection between the Computer and Media industry and other industries. FDG copulas, tractable and flexible, suits well for high-dimensional estimation. Potential of its application to other fields remains to be discovered.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1978/1/012045
  • Source: IOP Publishing Free Content
    IOPscience (Open Access)
    GFMER Free Medical Journals
    Coronavirus Research Database
    ProQuest Central
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