Nonlinearity in Stock Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00494143" target="_blank" >RIV/67985807:_____/18:00494143 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1063/1.5023309" target="_blank" >http://dx.doi.org/10.1063/1.5023309</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.5023309" target="_blank" >10.1063/1.5023309</a>

Result language

angličtina

Original language name

Nonlinearity in Stock Networks

Original language description

Stock networks, constructed from stock price time series, are a well-established tool for the characterization of complex behavior in stock markets. Following Mantegna’s seminal paper, the linear Pearson’s correlation coefficient between pairs of stocks has been the usual way to determine network edges. Recently, possible effects of nonlinearity on the graph-theoretical properties of such networks have been demonstrated when using nonlinear measures such as mutual information instead of linear correlation. In this paper, we quantitatively characterize the nonlinearity in stock time series and the effect it has on stock network properties. This is achieved by a systematic multi-step approach that allows us to quantify the nonlinearity of coupling, correct its effects wherever it is caused by simple univariate non-Gaussianity, potentially localize in space and time any remaining strong sources of this nonlinearity, and, finally, study the effect nonlinearity has on global network properties. By applying this multi-step approach to stocks included in three prominent indices (New York Stock Exchange 100, Financial Times Stock Exchange 100, and Standard & Poor 500), we establish that the apparent nonlinearity that has been observed is largely due to univariate non-Gaussianity. Furthermore, strong nonstationarity in a few specific stocks may play a role. In particular, the sharp decrease in some stocks during the global financial crisis of 2008 gives rise to apparent nonlinear dependencies among stocks.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Chaos

ISSN

1054-1500

e-ISSN

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Volume of the periodical

28

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

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UT code for WoS article

000443760700032

EID of the result in the Scopus database

2-s2.0-85052956242