Robust multivariate and functional archetypal analysis with application to financial time series analysis

Jesús Moliner and Irene Epifanio

Physica A: Statistical Mechanics and its Applications, 2019, vol. 519, issue C, 195-208

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 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.

Keywords: Multivariate functional data; Archetype analysis; Stock; M-estimators; Multivariate time series (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:519:y:2019:i:c:p:195-208

DOI: 10.1016/j.physa.2018.12.036

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