Extraction of the Underlying Structure of Systematic Risk from Non-Gaussian Multivariate Financial Time Series Using Independent Component Analysis: Evidence from the Mexican Stock Exchange
Título del documento: Extraction of the Underlying Structure of Systematic Risk from Non-Gaussian Multivariate Financial Time Series Using Independent Component Analysis: Evidence from the Mexican Stock Exchange
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000560399
ISSN: 1405-5546
Autores: 1
2
3
Instituciones: 1Universidad Veracruzana, Institute for Research and Graduate Studies in Administrative Sciences, México
2Universitat de Barcelona, Faculty of Economics and Business, Cataluña. España
3Polytechnic University of Catalonia, Barcelona School of Telecommunications Engineering, España
Año:
Periodo: Oct-Dic
Volumen: 22
Número: 4
Paginación: 1049-1064
País: México
Idioma: Inglés
Resumen en inglés Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.
Keyword: Extraction techniques,
Underlying risk factors,
Independent component analysis,
Arbitrage pricing theory,
Mexican stock exchange
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