Bivariate Functional Archetypoid Analysis: An Application to Financial Time Series
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Bivariate Functional Archetypoid Analysis: An Application to Financial Time SeriesAutoria
Tutor/Supervisor; Universitat.Departament
Epifanio López, Irene; Universitat Jaume I. Departament de MatemàtiquesData de publicació
2017-11-23Editor
Universitat Jaume IResum
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 ... [+]
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. [-]
Paraules clau / Matèries
Descripció
Treball de Fi de Màster Universitari en Matemàtica Computacional (Pla de 2013). Codi: SIQ027. Curs 2016-2017
Tipus de document
info:eu-repo/semantics/masterThesisDrets d'accés
info:eu-repo/semantics/openAccess
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