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

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  • Rogelio Ladrón de Guevara Cortés
  • Salvador Torra Porras
  • Enric Monte Moreno

Abstract

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.

Suggested Citation

  • Rogelio Ladrón de Guevara Cortés & Salvador Torra Porras & Enric Monte Moreno, 2021. "Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 13(2), pages 513-543.
    • Handle: RePEc:col:000443:019744
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    File URL: https://revfinypolecon.ucatolica.edu.co/article/view/3740
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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