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Instability of networks: effects of sampling frequency and extreme fluctuations in financial data
ABSTRACT: What determines the stability of networks inferred from dynamical behavior of a system? Internal and external shocks in a system can destabilize the topological properties of comovement networks. In real-world data, this creates a trade-off between identification of turbulent periods and t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035503/ https://www.ncbi.nlm.nih.gov/pubmed/35496353 http://dx.doi.org/10.1140/epjb/s10051-022-00332-x |
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author | Bhachech, Jalshayin Chakrabarti, Arnab Kaizoji, Taisei Chakrabarti, Anindya S. |
author_facet | Bhachech, Jalshayin Chakrabarti, Arnab Kaizoji, Taisei Chakrabarti, Anindya S. |
author_sort | Bhachech, Jalshayin |
collection | PubMed |
description | ABSTRACT: What determines the stability of networks inferred from dynamical behavior of a system? Internal and external shocks in a system can destabilize the topological properties of comovement networks. In real-world data, this creates a trade-off between identification of turbulent periods and the problem of high dimensionality. Longer time-series reduces the problem of high dimensionality, but suffers from mixing turbulent and non-turbulent periods. Shorter time-series can identify periods of turbulence more accurately, but introduces the problem of high dimensionality, so that the underlying linkages cannot be estimated precisely. In this paper, we exploit high-frequency multivariate financial data to analyze the origin of instability in the inferred networks during periods free from external disturbances. We show that the topological properties captured via centrality ordering is highly unstable even during such non-turbulent periods. Simulation results with multivariate Gaussian and fat-tailed stochastic process calibrated to financial data show that both sampling frequencies and the presence of outliers cause instability in the inferred network. We conclude that instability of network properties do not necessarily indicate systemic instability. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-9035503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-90355032022-04-25 Instability of networks: effects of sampling frequency and extreme fluctuations in financial data Bhachech, Jalshayin Chakrabarti, Arnab Kaizoji, Taisei Chakrabarti, Anindya S. Eur Phys J B Regular Article - Statistical and Nonlinear Physics ABSTRACT: What determines the stability of networks inferred from dynamical behavior of a system? Internal and external shocks in a system can destabilize the topological properties of comovement networks. In real-world data, this creates a trade-off between identification of turbulent periods and the problem of high dimensionality. Longer time-series reduces the problem of high dimensionality, but suffers from mixing turbulent and non-turbulent periods. Shorter time-series can identify periods of turbulence more accurately, but introduces the problem of high dimensionality, so that the underlying linkages cannot be estimated precisely. In this paper, we exploit high-frequency multivariate financial data to analyze the origin of instability in the inferred networks during periods free from external disturbances. We show that the topological properties captured via centrality ordering is highly unstable even during such non-turbulent periods. Simulation results with multivariate Gaussian and fat-tailed stochastic process calibrated to financial data show that both sampling frequencies and the presence of outliers cause instability in the inferred network. We conclude that instability of network properties do not necessarily indicate systemic instability. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2022-04-25 2022 /pmc/articles/PMC9035503/ /pubmed/35496353 http://dx.doi.org/10.1140/epjb/s10051-022-00332-x Text en © The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Regular Article - Statistical and Nonlinear Physics Bhachech, Jalshayin Chakrabarti, Arnab Kaizoji, Taisei Chakrabarti, Anindya S. Instability of networks: effects of sampling frequency and extreme fluctuations in financial data |
title | Instability of networks: effects of sampling frequency and extreme fluctuations in financial data |
title_full | Instability of networks: effects of sampling frequency and extreme fluctuations in financial data |
title_fullStr | Instability of networks: effects of sampling frequency and extreme fluctuations in financial data |
title_full_unstemmed | Instability of networks: effects of sampling frequency and extreme fluctuations in financial data |
title_short | Instability of networks: effects of sampling frequency and extreme fluctuations in financial data |
title_sort | instability of networks: effects of sampling frequency and extreme fluctuations in financial data |
topic | Regular Article - Statistical and Nonlinear Physics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035503/ https://www.ncbi.nlm.nih.gov/pubmed/35496353 http://dx.doi.org/10.1140/epjb/s10051-022-00332-x |
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