Robust multivariate and functional archetypal analysis with application to financial time series analysis
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Title
Robust multivariate and functional archetypal analysis with application to financial time series analysisDate
2018-12Publisher
ElsevierBibliographic citation
MOLINER, Jesús; EPIFANIO, Irene. Robust multivariate and functional archetypal analysis with application to financial time series analysis. Physica A: Statistical Mechanics and its Applications, 2019, 519: 195-208.Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S0378437118315498?via%3Dihub#a ...Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
Archetypal analysis approximates data by means of mixtures of actual extreme cases (archetypoids) or archetypes, which are a convex combination of cases in the data set. Archetypes lie on the boundary of the convex ... [+]
Archetypal analysis approximates data by means of mixtures of actual extreme cases (archetypoids) or archetypes, which are a convex combination of cases in the data set. Archetypes lie on the boundary of the convex hull. This makes the analysis very sensitive to outliers. A robust methodology by means of M-estimators for classical multivariate and functional data is proposed. This unsupervised methodology allows complex data to be understood even by non-experts. The performance of the new procedure is assessed in a simulation study, where a comparison with a previous methodology for the multivariate case is also carried out, and our proposal obtains favorable results. Finally, robust bivariate functional archetypoid analysis is applied to a set of companies in the S&P 500 described by two time series of stock quotes. A new graphic representation is also proposed to visualize the results. The analysis shows how the information can be easily interpreted and how even non-experts can gain a qualitative understanding of the data. [-]
Description
The code and data for reproducing the examples are available at http://www3.uji.es/epifanio/RESEARCH/rofada.rar. A preliminary version of this work was presented at the 8th International Conference on Mathematical and ... [+]
The code and data for reproducing the examples are available at http://www3.uji.es/epifanio/RESEARCH/rofada.rar. A preliminary version of this work was presented at the 8th International Conference on Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2018) (Moliner and Epifanio (2018)), where the application data were analyzed in a non-robust way. [-]
Investigation project
Spanish Ministry of Science, Innovation and Universities (AEI/FEDER, EU) (DPI2017-87333-R) ; Universitat Jaume I (UJI-B2017-13).Rights
© 2018 Elsevier B.V. All rights reserved.
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