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
Document title: 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
Journal: Computación y sistemas
Database:
System number: 000560399
ISSN: 1405-5546
Authors: 1
2
3
Institutions: 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
Year:
Season: Oct-Dic
Volumen: 22
Number: 4
Pages: 1049-1064
Country: México
Language: Inglés
Document type: Artículo
English abstract 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.
Disciplines: Ciencias de la computación
Keyword: Procesamiento de datos
Keyword: Extraction techniques,
Underlying risk factors,
Independent component analysis,
Arbitrage pricing theory,
Mexican stock exchange,
Data processing
Full text: Texto completo (Ver HTML) Texto completo (Ver PDF)