Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange

This dissertation focuses on the estimation of the generative multifactor model of returns on equities, under a statistical approach of the Arbitrage Pricing Theory (APT), in the context of the Mexican Stock Exchange. Therefore, this research takes as frameworks two main issues: (i) the multifactor...

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Autor: Ladrón de Guevara Cortés, Rogelio
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:CBUC, CESCA
Repositorio:TDR. Tesis Doctorales en Red
OAI Identifier:oai:www.tdx.cat:10803/386545
Acceso en línea:http://hdl.handle.net/10803/386545
Access Level:acceso abierto
Palabra clave:Models economètrics
Modelos econométricos
Econometric models
Inversions
Inversiones
Investments
Anàlisi multivariable
Análisis multivariante
Multivariate analysis
Intel·ligència artificial
Inteligencia artificial
Artificial intelligence
Ciències Jurídiques, Econòmiques i Socials
33
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oai_identifier_str oai:www.tdx.cat:10803/386545
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
title Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
spellingShingle Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
Ladrón de Guevara Cortés, Rogelio
Models economètrics
Modelos econométricos
Econometric models
Inversions
Inversiones
Investments
Anàlisi multivariable
Análisis multivariante
Multivariate analysis
Intel·ligència artificial
Inteligencia artificial
Artificial intelligence
Ciències Jurídiques, Econòmiques i Socials
33
title_short Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
title_full Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
title_fullStr Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
title_full_unstemmed Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
title_sort Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
dc.creator.none.fl_str_mv Ladrón de Guevara Cortés, Rogelio
author Ladrón de Guevara Cortés, Rogelio
author_facet Ladrón de Guevara Cortés, Rogelio
author_role author
dc.contributor.none.fl_str_mv Torra Porras, Salvador
Torra Porras, Salvador
Universitat de Barcelona. Departament d'Econometria, Estadística i Economia Espanyola
dc.subject.none.fl_str_mv Models economètrics
Modelos econométricos
Econometric models
Inversions
Inversiones
Investments
Anàlisi multivariable
Análisis multivariante
Multivariate analysis
Intel·ligència artificial
Inteligencia artificial
Artificial intelligence
Ciències Jurídiques, Econòmiques i Socials
33
topic Models economètrics
Modelos econométricos
Econometric models
Inversions
Inversiones
Investments
Anàlisi multivariable
Análisis multivariante
Multivariate analysis
Intel·ligència artificial
Inteligencia artificial
Artificial intelligence
Ciències Jurídiques, Econòmiques i Socials
33
description This dissertation focuses on the estimation of the generative multifactor model of returns on equities, under a statistical approach of the Arbitrage Pricing Theory (APT), in the context of the Mexican Stock Exchange. Therefore, this research takes as frameworks two main issues: (i) the multifactor asset pricing models, specially the statistical risk factors approach, and (ii) the dimension reduction or feature extraction techniques: Principal Component Analysis, Factor Analysis, Independent Component Analysis and Non-linear Principal Component Analysis, utilized to extract the underlying systematic risk factors. The models estimated are tested using two methodologies: (i) capability of reproduction of the observed returns using the estimated generative multifactor model, and (ii) results of the econometric contrast of the APT using the extracted systematic risk factors. Finally, a comparative study among techniques is carried on based on their theoretical properties and the empirical results. According to the above stated and as far as we concerned, this dissertation contributes to financial research by providing empirical evidence of the estimation of the generative multifactor model of returns on equities, extracting statistical underlying risk factors via classic and alternative dimension reduction or feature extraction techniques in the field of finance, in order to test the APT as an asset pricing model, in the context of an emerging financial market such as the Mexican Stock Exchange. In addition, this work presents an unprecedented theoretical and empirical comparative study among Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, as techniques to extract systematic risk factors from a stock exchange, analyzing the level of sensitivity of the results in function of the technique carried on. In addition, this dissertation represents a mainly empirical exhaustive study where objective evidence about the Mexican stock market is provided by way of the application of four different techniques for extraction of systematic risk factors, to four datasets, in a test window that ranged from two to nine factors.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10803/386545
url http://hdl.handle.net/10803/386545
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 646 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat de Barcelona
publisher.none.fl_str_mv Universitat de Barcelona
dc.source.none.fl_str_mv TDX (Tesis Doctorals en Xarxa)
reponame:TDR. Tesis Doctorales en Red
instname:CBUC, CESCA
instname_str CBUC, CESCA
reponame_str TDR. Tesis Doctorales en Red
collection TDR. Tesis Doctorales en Red
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock ExchangeLadrón de Guevara Cortés, RogelioModels economètricsModelos econométricosEconometric modelsInversionsInversionesInvestmentsAnàlisi multivariableAnálisis multivarianteMultivariate analysisIntel·ligència artificialInteligencia artificialArtificial intelligenceCiències Jurídiques, Econòmiques i Socials33This dissertation focuses on the estimation of the generative multifactor model of returns on equities, under a statistical approach of the Arbitrage Pricing Theory (APT), in the context of the Mexican Stock Exchange. Therefore, this research takes as frameworks two main issues: (i) the multifactor asset pricing models, specially the statistical risk factors approach, and (ii) the dimension reduction or feature extraction techniques: Principal Component Analysis, Factor Analysis, Independent Component Analysis and Non-linear Principal Component Analysis, utilized to extract the underlying systematic risk factors. The models estimated are tested using two methodologies: (i) capability of reproduction of the observed returns using the estimated generative multifactor model, and (ii) results of the econometric contrast of the APT using the extracted systematic risk factors. Finally, a comparative study among techniques is carried on based on their theoretical properties and the empirical results. According to the above stated and as far as we concerned, this dissertation contributes to financial research by providing empirical evidence of the estimation of the generative multifactor model of returns on equities, extracting statistical underlying risk factors via classic and alternative dimension reduction or feature extraction techniques in the field of finance, in order to test the APT as an asset pricing model, in the context of an emerging financial market such as the Mexican Stock Exchange. In addition, this work presents an unprecedented theoretical and empirical comparative study among Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, as techniques to extract systematic risk factors from a stock exchange, analyzing the level of sensitivity of the results in function of the technique carried on. In addition, this dissertation represents a mainly empirical exhaustive study where objective evidence about the Mexican stock market is provided by way of the application of four different techniques for extraction of systematic risk factors, to four datasets, in a test window that ranged from two to nine factors.Universitat de BarcelonaTorra Porras, SalvadorTorra Porras, SalvadorUniversitat de Barcelona. Departament d'Econometria, Estadística i Economia Espanyola201620162016info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersion646 p.application/pdfapplication/pdfhttp://hdl.handle.net/10803/386545TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/3865452026-06-14T12:46:07Z
score 15,301603

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