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Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced u...

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Autores principales: Kazakevičius, Rytis, Kononovicius, Aleksejus, Kaulakys, Bronislovas, Gontis, Vygintas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470578/
https://www.ncbi.nlm.nih.gov/pubmed/34573750
http://dx.doi.org/10.3390/e23091125
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author Kazakevičius, Rytis
Kononovicius, Aleksejus
Kaulakys, Bronislovas
Gontis, Vygintas
author_facet Kazakevičius, Rytis
Kononovicius, Aleksejus
Kaulakys, Bronislovas
Gontis, Vygintas
author_sort Kazakevičius, Rytis
collection PubMed
description In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations, and agent-based models—reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first-passage time distributions. Research has lead us to question whether the observed long-range memory is a result of the actual long-range memory process or just a consequence of the non-linearity of Markov processes. As our most recent result, we discuss the long-range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the auto-regressive fractionally integrated moving average (ARFIMA) sample series. Our newly obtained results seem to indicate that new estimators of self-similarity and long-range memory for analyzing systems with non-Gaussian distributions have to be developed.
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spelling pubmed-84705782021-09-27 Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems Kazakevičius, Rytis Kononovicius, Aleksejus Kaulakys, Bronislovas Gontis, Vygintas Entropy (Basel) Article In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations, and agent-based models—reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first-passage time distributions. Research has lead us to question whether the observed long-range memory is a result of the actual long-range memory process or just a consequence of the non-linearity of Markov processes. As our most recent result, we discuss the long-range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the auto-regressive fractionally integrated moving average (ARFIMA) sample series. Our newly obtained results seem to indicate that new estimators of self-similarity and long-range memory for analyzing systems with non-Gaussian distributions have to be developed. MDPI 2021-08-29 /pmc/articles/PMC8470578/ /pubmed/34573750 http://dx.doi.org/10.3390/e23091125 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kazakevičius, Rytis
Kononovicius, Aleksejus
Kaulakys, Bronislovas
Gontis, Vygintas
Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems
title Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems
title_full Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems
title_fullStr Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems
title_full_unstemmed Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems
title_short Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems
title_sort understanding the nature of the long-range memory phenomenon in socioeconomic systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470578/
https://www.ncbi.nlm.nih.gov/pubmed/34573750
http://dx.doi.org/10.3390/e23091125
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