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Journal articles on the topic 'Econophysics'

To see the other types of publications on this topic, follow the link: Econophysics.

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

GINGRAS, Y., and C. SCHINCKUS. "THE INSTITUTIONALIZATION OF ECONOPHYSICS IN THE SHADOW OF PHYSICS." Journal of the History of Economic Thought 34, no. 1 (March 2012): 109–30. http://dx.doi.org/10.1017/s1053837212000041.

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Econophysics presents itself as a new paradigm and a new specialty (or even a discipline) using various models and concepts imported from condensed matter and statistical physics to analyze economic and financial phenomena. Given that econophysics is based on different fundamental assumptions from those of mainstream economics, the disciplinary position of econophysics is not so clear. In this perspective, this paper will analyze the progressive institutionalization of econophysics using bibliometric methods to identify core authors as well as the structure of the disciplines with which econophysics is closely related.

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2

Schinckus, C. "Methodological comment on Econophysics review I and II: statistical econophysics and agent-based econophysics." Quantitative Finance 12, no. 8 (August 2012): 1189–92. http://dx.doi.org/10.1080/14697688.2012.704692.

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3

Ghosh, Bikramaditya, and M. C. Krishna. "Power law in tails of bourse volatility – evidence from India." Investment Management and Financial Innovations 16, no. 1 (March 26, 2019): 291–98. http://dx.doi.org/10.21511/imfi.16(1).2019.23.

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Inverse cubic law has been an established Econophysics law. However, it has been only carried out on the distribution tails of the log returns of different asset classes (stocks, commodities, etc.). Financial Reynolds number, an Econophysics proxy for bourse volatility has been tested here with Hill estimator to find similar outcome. The Tail exponent or α ≈ 3, is found to be well outside the Levy regime (0 &lt; α &lt; 2). This confirms that asymptotic decay pattern for the cumulative distribution in fat tails following inverse cubic law. Hence, volatility like stock returns also follow inverse cubic law, thus stay way outside the Levy regime. This piece of work finds the volatility proxy (econophysical) to be following asymptotic decay with tail exponent or α ≈ 3, or, in simple terms, ‘inverse cubic law’. Risk (volatility proxy) and return (log returns) being two inseparable components of quantitative finance have been found to follow the similar law as well. Hence, inverse cubic law truly becomes universal in quantitative finance.

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4

Argyrakis, P. "What is EconoPhysics?" Journal of Engineering Science and Technology Review 4, no. 3 (December 2011): 207–8. http://dx.doi.org/10.25103/jestr.043.01.

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5

JUNG, Woo-Sung, and Seunghwan KIM. "Introduction to Econophysics." Physics and High Technology 31, no. 7/8 (August 31, 2022): 3–5. http://dx.doi.org/10.3938/phit.31.022.

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6

Mimkes, Juergen. "Introduction to Econophysics." International Journal of Productivity Management and Assessment Technologies 7, no. 1 (January 2019): 1–27. http://dx.doi.org/10.4018/ijpmat.2019010101.

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The future contains terms (V) that are valid at all times, and terms (U) that are presently unknown. In economics, (V) and (U) correspond to ex ante and ex post, in physics to conservative and not conservative, in calculus, to exact and not exact differential forms or to Riemann and Stokes integrals, and to linear or non-linear equations, in statistics to real and probable terms. Apparently, the authors may represent the (V) and (U) structure in economics and physics by calculus, probability theory, by non-linearity, and chaos theory. The present paper applies Stokes integrals to double entry accounting. The resulting laws replace neoclassical theory and correspond to the first and second laws of thermodynamics. Economics and physics have the same structure, leading to the name econophysics. Production is a two level cycle with cheap production, expensive sales, corresponding to the Carnot cycle of a motor with cold air and hot exhaust. In a running motor, efficiency, the difference between hot and cold, becomes always higher. In an economy the gap between rich and poor always rises.

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7

Rosser, J. Barkley. "Entropy and econophysics." European Physical Journal Special Topics 225, no. 17-18 (December 2016): 3091–104. http://dx.doi.org/10.1140/epjst/e2016-60166-y.

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8

Rickles, Dean. "Econophysics for philosophers." Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 38, no. 4 (December 2007): 948–78. http://dx.doi.org/10.1016/j.shpsb.2007.01.003.

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9

Hategan, Sergiu Mihai. "A MAPPING OF THE LITERATURE ON ECONOPHYSICS." Annals of the University of Oradea. Economic Sciences 30, no. 30 (1) (July 2021): 92–100. http://dx.doi.org/10.47535/1991auoes30(1)008.

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Econophysics is a relatively young discipline, being an interdisciplinary approach that applies methods and tools from physics in the economics, for studying the financial markets, or other economical phenomena. The objective is to create a mapping of the most used keywords in this topic, as well as the connections between countries and authors, based on citations. This article studies the literature on the topic of econophysics, using the Web of Science database. Bibliometric analysis was made with the free software VOSviewer, after extracting the data from 1364 articles, since the conception of the topic in 1996 until the year 2020. The software offered a useful network representation between the main keywords used in the field, or between countries and citations, and finally between authors, based on citations. The results evidenced a constant interest on the topic, with main keywords: ”econophysics”, ”financial markets”, ”dynamics”, ”model” and ”stock market”, and the relationships between countries and authors were shown, with the United States, China and Italy being the leading ones. We also found that the most cited authors are H.E. Stanley, the inventor of the word econophysics, Wei-Xing Zhou and Didier Sornette. This article shows the main concepts used in econophysics, which can serve as an indicator of its directions of research, as well as the top contributors in the field, since its inception. Future directions include expanding the analysis to other databases, or to concepts relevant to econophysics, to identify trends in research and the growth of the field.

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10

Stavrinides, Stavros, Michael Hanias, Boryana Bogdanova, and Lykourgos Magafas. "Special Issue on Econophysics." Journal of Engineering Science and Technology Review 8, no. 1 (February 2015): i—j. http://dx.doi.org/10.25103/jestr.081.00.

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11

SINGH, RAJINDER. "THE STORY OF ECONOPHYSICS." Science and Culture 86, no. 5-6 (June 7, 2020): 188. http://dx.doi.org/10.36094/sc.v86.2020.the_story_of_econophysics.singh.188.

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12

Hanias, M. P., L. Magafas, and S. G. Stavrinides. "“Reverse Engineering” in Econophysics." International Journal of Productivity Management and Assessment Technologies 7, no. 1 (January 2019): 36–49. http://dx.doi.org/10.4018/ijpmat.2019010103.

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The work presented here is a paradigm of EconoPhysics, i.e. of research in the area of finance and economics by applying physical models, in this case chaos theory. A specific analysis of a macroeconomic model proposed by Vosvrda is presented. The Vosvrda model is an idealized macroeconomic model, combining the savings of households, Gross Domestic Product and the foreign capital inflow. It is simulated by three autonomous differential equations. According to this model, there are six parameters, having their values regulating the system behavior (parameters of Vosvdra). Using artificial noisy data for simulating real data and using an inverse modelling procedure, the authors have fitted and tuned the parameters of Vosvdra differential equations to achieve more accurate solutions. The relevant resultant evaluation showed that the system is a chaotic one, even though for the same values proposed by Vosvrda. Finally, this chaotic behavior has provided the capability to expand the time horizon of the solution, thus achieving reliable forecasting for the system.

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13

Pueyo, Salvador. "Ecological Econophysics for Degrowth." Sustainability 6, no. 6 (May 28, 2014): 3431–83. http://dx.doi.org/10.3390/su6063431.

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14

Johnson, Simon. "The trouble with econophysics." Physics World 25, no. 01 (January 2012): 17. http://dx.doi.org/10.1088/2058-7058/25/01/25.

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15

ROSSER, J. BARKLEY. "ECONOPHYSICS AND ECONOMIC COMPLEXITY." Advances in Complex Systems 11, no. 05 (October 2008): 745–60. http://dx.doi.org/10.1142/s0219525908001957.

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This paper discusses the debate between those advocating a computational and those advocating a dynamic definition of complexity, and how this relates to issues in econophysics. It then reviews the criticisms that have been raised about ways in which econophysics has been done, noting that many of these are now being dealt with. Finally, it argues that while an obvious way to resolve many of these matters is to have economists and physicists work together, the physicists should be sure to work with economists who understand the complexity critique of conventional economic theory and are thus not led astray into building models that have some of the problems of standard economics models that most econophysicists have striven to overcome.

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16

Hunter, Chris. "Econophysics and Physical Economics." Quantitative Finance 16, no. 4 (February 3, 2016): 513–15. http://dx.doi.org/10.1080/14697688.2015.1112970.

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17

de Area Leão Pereira, Eder Johnson, Marcus Fernandes da Silva, and H. B. B. Pereira. "Econophysics: Past and present." Physica A: Statistical Mechanics and its Applications 473 (May 2017): 251–61. http://dx.doi.org/10.1016/j.physa.2017.01.007.

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18

Bordley, Robert F. "Econophysics and individual choice." Physica A: Statistical Mechanics and its Applications 354 (August 2005): 479–95. http://dx.doi.org/10.1016/j.physa.2005.03.016.

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19

Gallegati, Mauro, Steve Keen, Thomas Lux, and Paul Ormerod. "Worrying trends in econophysics." Physica A: Statistical Mechanics and its Applications 370, no. 1 (October 2006): 1–6. http://dx.doi.org/10.1016/j.physa.2006.04.029.

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20

Schinckus, Christophe. "Economic uncertainty and econophysics." Physica A: Statistical Mechanics and its Applications 388, no. 20 (October 2009): 4415–23. http://dx.doi.org/10.1016/j.physa.2009.07.008.

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21

Ausloos, M. "Special issue on Econophysics." European Physical Journal B 20, no. 4 (April 2001): 471. http://dx.doi.org/10.1007/s100510170225.

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22

BALI, Selçuk. "Econophysics, Thermoeconomics and Phynance." Journal of International Social Research 4, no. 18 (July 20, 2011): 379. http://dx.doi.org/10.17719/jisr.20111811038.

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23

Schinckus, Christophe, and Franck Jovanovic. "Towards a transdisciplinary econophysics." Journal of Economic Methodology 20, no. 2 (June 2013): 164–83. http://dx.doi.org/10.1080/1350178x.2013.801561.

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24

Shaikh, Anwar. "The econ in econophysics." European Physical Journal Special Topics 229, no. 9 (July 2020): 1675–84. http://dx.doi.org/10.1140/epjst/e2020-900204-5.

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25

Shubik, Martin, and Eric Smith. "Econophysics: Present and future." Complexity 14, no. 3 (January 2009): 9–10. http://dx.doi.org/10.1002/cplx.20260.

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26

KAUFMAN, MIRON. "On an Econophysics Model." Turkish Journal of Physics 46, no. 6 (January 1, 2022): 188–93. http://dx.doi.org/10.55730/1300-0101.2725.

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27

Schinckus, Christophe, and Cinla Akdere. "Duality of knowledge, singularity of method." Journal of Asian Business and Economic Studies 25, no. 1 (June 11, 2018): 163–84. http://dx.doi.org/10.1108/jabes-05-2018-0010.

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Purpose How a micro-founded discipline such as economics could deal with the increasing global economic reality? This question has been asked frequently since the last economic crisis that appeared in 2008. In this challenging context, some commentators have turned their attention to a new area of knowledge coming from physics: econophysics which mainly focuses on a macro-analysis of economic systems. By showing that concepts used by econophysicists are consistent with an existing economic knowledge (developed by J.S. Mill), the purpose of this paper is to claim that an interdisciplinary perspective is possible between these two communities. Design/methodology/approach The authors propose a historical and conceptual analysis of the key concept of emergence to emphasize the potential bridge between econophysics and economics. Findings Six methodological arguments will be developed in order to show the existence of conceptual bridges as a necessary condition for the elaboration of a common language between economists and econophysics which would not be superfluous, in this challenging context, to clarify the growing complexity of economic phenomena. Originality/value Although the economics and econophysics study same the complex economic phenomena, very few collaborations exist between them. This paper paves a conceptual/methodological path for more collaboration between the two fields.

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28

Ferreira, Paulo, Éder J. A. L. Pereira, and Hernane B. B. Pereira. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena." Journal of Risk and Financial Management 13, no. 7 (July 13, 2020): 153. http://dx.doi.org/10.3390/jrfm13070153.

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Big data has become a very frequent research topic, due to the increase in data availability. In this introductory paper, we make the linkage between the use of big data and Econophysics, a research field which uses a large amount of data and deals with complex systems. Different approaches such as power laws and complex networks are discussed, as possible frameworks to analyze complex phenomena that could be studied using Econophysics and resorting to big data.

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29

Rosser-Jr., J. B. "The Nature and Future of Econophysics." Voprosy Ekonomiki, no. 11 (November 20, 2009): 76–81. http://dx.doi.org/10.32609/0042-8736-2009-11-76-81.

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The article presents an overview of econophysics, its main issues, history and possible paths of future development. Descriptions of the subject, problems and methodology are provided. The questions are raised such as what the contribution of the new discipline to the core issues in economics is and whether the new methods and approaches could be applied in physics. The role of the interdisciplinary research which tends to become the main engine of development in econophysics is discussed in detail.

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30

Tusset, Gianfranco. "Plotting the Words of Econophysics." Entropy 23, no. 8 (July 23, 2021): 944. http://dx.doi.org/10.3390/e23080944.

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Text mining is applied to 510 articles on econophysics to reconstruct the lexical evolution of the discipline from 1999 to 2020. The analysis of the relative frequency of the words used in the articles and their “visualization” allow us to draw some conclusions about the evolution of the discipline. The traditional areas of research, financial markets and distribution of wealth, remain central, but they are flanked by other strands of research—production, currencies, networks—which broaden the discipline by pushing towards a dialectical application of traditional concepts and tools drawn from statistical physics.

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31

Di Matteo, Tiziana, and Tomaso Aste. "“No Worries": Trends in Econophysics." European Physical Journal B 55, no. 2 (January 2007): 121–22. http://dx.doi.org/10.1140/epjb/e2007-00047-1.

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32

Yakovenko, Victor M. "Monetary economics from econophysics perspective." European Physical Journal Special Topics 225, no. 17-18 (December 2016): 3313–35. http://dx.doi.org/10.1140/epjst/e2016-60213-3.

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33

Shaikh, Anwar. "Income Distribution, Econophysics and Piketty." Review of Political Economy 29, no. 1 (July 19, 2016): 18–29. http://dx.doi.org/10.1080/09538259.2016.1205295.

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34

Chakraborti, Anirban, Ioane Muni Toke, Marco Patriarca, and Frédéric Abergel. "Econophysics review: I. Empirical facts." Quantitative Finance 11, no. 7 (June 24, 2011): 991–1012. http://dx.doi.org/10.1080/14697688.2010.539248.

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35

Ray, Russ. "Econophysics: finance, economics and physics." Applied Economics Letters 18, no. 3 (February 2011): 273–77. http://dx.doi.org/10.1080/13504851003596020.

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36

Schinckus, Christophe. "Ising model, econophysics and analogies." Physica A: Statistical Mechanics and its Applications 508 (October 2018): 95–103. http://dx.doi.org/10.1016/j.physa.2018.05.063.

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37

Richards, Gordon R. "Reconciling econophysics with macroeconomic theory." Physica A: Statistical Mechanics and its Applications 282, no. 1-2 (July 2000): 325–35. http://dx.doi.org/10.1016/s0378-4371(00)00112-6.

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38

Zapart, Christopher A. "Econophysics: A challenge to econometricians." Physica A: Statistical Mechanics and its Applications 419 (February 2015): 318–27. http://dx.doi.org/10.1016/j.physa.2014.10.013.

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39

Schinckus, Christophe. "Econophysics and economics: Sister disciplines?" American Journal of Physics 78, no. 4 (April 2010): 325–27. http://dx.doi.org/10.1119/1.3309424.

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40

Kutner, Ryszard, Christophe Schinckus, and Harry Eugene Stanley. "Three Risky Decades: A Time for Econophysics?" Entropy 24, no. 5 (April 29, 2022): 627. http://dx.doi.org/10.3390/e24050627.

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41

FAN, YING, MENGHUI LI, JIAWEI CHEN, LIANG GAO, ZENGRU DI, and JINSHAN WU. "NETWORK OF ECONOPHYSICISTS: A WEIGHTED NETWORK TO INVESTIGATE THE DEVELOPMENT OF ECONOPHYSICS." International Journal of Modern Physics B 18, no. 17n19 (July 30, 2004): 2505–11. http://dx.doi.org/10.1142/s0217979204025579.

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The development of Econophysics is studied from the perspective of scientific communication networks. Papers in Econophysics published from 1992 to 2003 are collected. Then a weighted and directed network of scientific communication, including collaboration, citation and personal discussion, is constructed. Its static geometrical properties, including degree distribution, weight distribution, weight per degree, and betweenness centrality, give a nice overall description of the research works. The way we introduced here to measure the weight of connections can be used as a general one to construct weighted network.

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42

Ikeda, Yuichi. "Special feature: Econophysics 2017: synergetic fusion of econophysics and other fields of science— Part I." Evolutionary and Institutional Economics Review 15, no. 2 (November 23, 2018): 461–63. http://dx.doi.org/10.1007/s40844-018-0118-5.

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43

Ikeda, Yuichi. "Special feature: Econophysics 2017: synergetic fusion of econophysics and other fields of science—Part II." Evolutionary and Institutional Economics Review 16, no. 1 (June 2019): 181–82. http://dx.doi.org/10.1007/s40844-019-00128-2.

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44

Bielinskyi, Andrii, Oleksandr Serdyuk, Serhiy Semerikov, and Vladimir Soloviev. "Econophysics of cryptocurrency crashes: an overview." SHS Web of Conferences 107 (2021): 03001. http://dx.doi.org/10.1051/shsconf/202110703001.

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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45

Islas-García, J. D. A., A. R. Villagómez-Manrique, Marcelo Del Castillo-Mussot, and P. G. Soriano-Hernandez. "Brownian motion, diffusion, entropy and econophysics." Revista Mexicana de Física E 65, no. 1 (January 21, 2019): 1. http://dx.doi.org/10.31349/revmexfise.65.1.

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To model wealth distributions there exist models based on the Boltzmann-Gibbs distribution (BGD), which is obtained by simulating binary economic interactions or exchanges that are similar to particle collisions in physics with conservedenergy (or money in econophysics). Also, BGD can be reproduced by numerical simulations of diffusion for many particles which experience energy fluctuations. This latter case is analogous to non-interacting pollen particles performing Brownian motion. In order to decrease inequality, we also modify the energy-conserved diffusion by taxing the richest agent. In all cases, we calculate the corresponding Gini inequality index and the time evolution of the entropy to show the stability of the statistical distributions.

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46

Herzog, Bodo. "An Econophysics Model of Financial Bubbles." Natural Science 07, no. 01 (2015): 55–63. http://dx.doi.org/10.4236/ns.2015.71006.

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47

Mostardinha, P., E. J. Durana, and F. Vistulo de Abreu. "The econophysics in the Euromillions lottery." European Journal of Physics 27, no. 3 (April 21, 2006): 675–84. http://dx.doi.org/10.1088/0143-0807/27/3/020.

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48

Chakraborti, Anirban, Ioane Muni Toke, Marco Patriarca, and Frédéric Abergel. "Econophysics review: II. Agent-based models." Quantitative Finance 11, no. 7 (June 24, 2011): 1013–41. http://dx.doi.org/10.1080/14697688.2010.539249.

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49

McCauley, Joseph L. "Response to “Worrying Trends in Econophysics”." Physica A: Statistical Mechanics and its Applications 371, no. 2 (November 2006): 601–9. http://dx.doi.org/10.1016/j.physa.2006.05.043.

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50

Carbone, A., G. Kaniadakis, and A. M. Scarfone. "Where do we stand on econophysics?" Physica A: Statistical Mechanics and its Applications 382, no. 1 (August 2007): xi—xiv. http://dx.doi.org/10.1016/j.physa.2007.05.054.

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