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Bivariate Functional Archetypoid Analysis: An Application to Financial Time Series
dc.contributor.author | Moliner Moliner, Jesús | |
dc.contributor.other | Epifanio López, Irene | |
dc.contributor.other | Universitat Jaume I. Departament de Matemàtiques | |
dc.date.accessioned | 2018-05-09T10:10:58Z | |
dc.date.available | 2018-05-09T10:10:58Z | |
dc.date.issued | 2017-11-23 | |
dc.identifier.uri | http://hdl.handle.net/10234/174594 | |
dc.description | Treball de Fi de Màster Universitari en Matemàtica Computacional (Pla de 2013). Codi: SIQ027. Curs 2016-2017 | ca_CA |
dc.description.abstract | Archetype Analysis (AA) is a statistical technique that describes individuals of a sample as a convex combination of certain number of elements called Archetypes, which in turn, are convex combinations of the individuals in the sample. For it's part, Archetypoid Analysis (ADA) tries to represent each individual as a convex combination of a certain number of extreme subjects called Archetypoids. It is possible to apply these techniques to functional data applying a basis expansion function and performing AA or ADA to the weighted coe cients in the basis. This document presents an application of Functional Archetypoids Analysis (FADA) to nancial time series. The starting time series consists of daily equity prices of the SP500 stocks. From it, measures of volatility and pro tability are generated in order to characterize listed companies. These variables are converted into functional data through a Fourier basis expansion function and bivariate FADA is applied. By representing subjects through extreme cases, this analysis facilitates the understanding of both the composition and the relationships between listed companies. Finally, a cluster methodology based on a similarity parameter is presented. Therefore, the suitability of this technique for this kind of time series is shown, as well as the robustness of the conclusions drawn. | ca_CA |
dc.format.extent | 39 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Universitat Jaume I | ca_CA |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Màster Universitari en Matemàtica Computacional | ca_CA |
dc.subject | Máster Universitario en Matemática Computacional | ca_CA |
dc.subject | Master's Degree in Computational Mathematics | ca_CA |
dc.subject | Archetype Analysis | ca_CA |
dc.subject | Archetypoid Analysis | ca_CA |
dc.subject | Statistical Learning | ca_CA |
dc.subject | Clustering | ca_CA |
dc.subject | Unsupervised Learning | ca_CA |
dc.subject | Financial Time Series | ca_CA |
dc.subject | Extremal point | ca_CA |
dc.title | Bivariate Functional Archetypoid Analysis: An Application to Financial Time Series | ca_CA |
dc.type | info:eu-repo/semantics/masterThesis | ca_CA |
dc.educationLevel | Estudios de Postgrado | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
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TFM: Màster Universitari en Matemàtica Computacional [51]
SIB027, SIQ026, SIQ027, SIQ526, SIQ527