Hierarchically nested factor model from multivariate data

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Abstract

We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically, we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap-based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.
Lingua originaleEnglish
pagine (da-a)30006-p1-30006-p6
Numero di pagine6
RivistaEurophysics Letters
Volume78
Stato di pubblicazionePublished - 2007

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All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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title = "Hierarchically nested factor model from multivariate data",
abstract = "We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically, we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap-based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.",
keywords = "Finance, commerce, hierarchical structure",
author = "Mantegna, {Rosario Nunzio} and Fabrizio Lillo and Michele Tumminello and Lillo",
year = "2007",
language = "English",
volume = "78",
pages = "30006--p1--30006--p6",
journal = "Europhysics Letters",
issn = "0295-5075",
publisher = "IOP Publishing Ltd.",

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T1 - Hierarchically nested factor model from multivariate data

AU - Mantegna, Rosario Nunzio

AU - Lillo, Fabrizio

AU - Tumminello, Michele

AU - Lillo, null

PY - 2007

Y1 - 2007

N2 - We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically, we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap-based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.

AB - We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically, we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap-based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.

KW - Finance, commerce, hierarchical structure

UR - http://hdl.handle.net/10447/14110

M3 - Article

VL - 78

SP - 30006-p1-30006-p6

JO - Europhysics Letters

JF - Europhysics Letters

SN - 0295-5075

ER -