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Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

Revista Finanzas y Política Económica, 2021-07, Vol.13 (2), p.513-543 [Peer Reviewed Journal]

2021. This work is published under https://creativecommons.org/licenses/by-nc-sa/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. ;ISSN: 2248-6046 ;EISSN: 2011-7663 ;DOI: 10.14718/revfinanzpolitecon.v13.n2.2021.9

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  • Title:
    Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange
  • Author: Rogelio ; Torra Porras, Salvador ; Monte Moreno, Enric
  • Subjects: Comparative studies ; Discriminant analysis ; Feature extraction ; Principal components analysis ; Risk factors ; SOCIAL SCIENCES, INTERDISCIPLINARY ; Stock exchanges
  • Is Part Of: Revista Finanzas y Política Económica, 2021-07, Vol.13 (2), p.513-543
  • Description: This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.
  • Publisher: Bogota: Universidad Católica de Colombia, Facultad de Ciencias Económicas y Administrativas
  • Language: English;Portuguese
  • Identifier: ISSN: 2248-6046
    EISSN: 2011-7663
    DOI: 10.14718/revfinanzpolitecon.v13.n2.2021.9
  • Source: SciELO
    Coronavirus Research Database
    ProQuest Central
    DOAJ Directory of Open Access Journals
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