Title Understanding the nature of the long-range memory phenomenon in socioeconomic systems /
Authors Kazakevičius, Rytis ; Kononovičius, Aleksejus ; Kaulakys, Bronislovas ; Gontis, Vygintas
DOI 10.3390/e23091125
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Is Part of Entropy.. Basel : MDPI. 2021, vol. 23, iss. 9, art. no. 1125, p. [1-30].. eISSN 1099-4300
Keywords [eng] long-range memory ; 1/f noise ; absolute value estimator ; anomalous diffusion ; ARFIMA ; first-passage times ; fractional Lèvy stable motion ; Higuchi’s method ; mean squared displacement ; multiplicative point process
Abstract [eng] 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.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2021
CC license CC license description