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
Revue: Computación y sistemas
Base de datos:
Número de sistema: 000560399
ISSN: 1405-5546
Autores: 1
2
3
Instituciones: 1Universidad Veracruzana, Instituto de Investigación y Estudios de Posgrado en Ciencias Administrativas, Xalapa, Veracruz. México
2Universitat de Barcelona, Facultad de Economía y Empresa, Barcelona. España
3Universidad Politécnica de Cataluña, Barcelona School of Telecommunications Engineering, Barcelona. España
Año:
Periodo: Oct-Dic
Volumen: 22
Número: 4
Paginación: 1049-1064
País: México
Idioma: Inglés
Tipo de documento: Artículo
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.
Disciplinas: Ciencias de la computación
Palabras clave: Procesamiento de datos
Keyword: Extraction techniques,
Underlying risk factors,
Independent component analysis,
Arbitrage pricing theory,
Mexican stock exchange,
Data processing
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