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Predictors of oil shocks. Econophysical approach in environmental science

Bielinskyi, A. O.; Khvostina, I.; Mamanazarov, A.; Matviychuk, A.; Семеріков, Сергій Олексійович; Serdyuk, O.; Solovieva, V.; Соловйов, Володимир Миколайович

URI: https://doi.org/10.1088/1755-1315/628/1/012019
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4352

Date: 2021-01-22

Abstract:

The instability of the price dynamics of the energy market from a theoretical point of view indicates the inadequacy of the dominant paradigm of the quantitative description of pricing processes, and from a practical point of view, it leads to abnormal shocks and crashes. A striking example is the COVID-stimulated spring drop of spot prices for crude oil by 305% to $36.73 a barrel. The theory of complex systems with the latest complex networking achievements using pragmatically verified econophysical approaches and models can become the basis of modern environmental science. In this case, it is possible to introduce certain measures of complexity, the change in the dynamics of which makes it possible to identify and prevent characteristic types of critical phenomena. In this paper, the possibility of using some econophysical approaches for quantitative assessment of complexity measures: (1) informational (Lempel-Ziv measure, various types of entropies (Shannon, Approximate, Permutation, Recurrence), (2) fractal and multifractal (Multifractal Detrended Fluctuation Analysis), (3) recurrent (Recurrence Plot and Recurrence Quantification Analysis), (4) Lévy's stable distribution properties, (5) network (Visual Graph and Recurrence based) and (6) quantum (Heisenberg uncertainty principle) is investigated. Each of them detects patterns that are general for crisis states. We conclude that these measures make it possible to establish that the socially responsive exhibits characteristic patterns of complexity and the proposed measures of complexity allow us to build indicators-precursors of critical and crisis phenomena. Proposed quantitative measures of complexity classified and adapted for the crude oil market. Their behavior in the face of known market shocks and 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 crude oil market.

Description:

[1] Albert R and Barabasi A-L 2002 Statistical Mechanics of Complex Networks Reviews of Modern Physics 74 47–97 [2] Aloui C, Hamdi M, Mensi W and Nguyen D 2012 Further evidence on the time-varying efficiency of crude oil markets Energy Stud. Rev. 19 39-51 [3] Amigo J M 2010 Permutation Complexity in Dynamical Systems. Ordinal Patterns, Permutation Entropy and All That (Berlin: Springer-Verlag) p 280 [4] Arnold V I and Avez A 1968 Ergodic problems of classical mechanics (N.-Y./Amsterdam: The Mathematical Physics Monograph Series) [5] Aslam F; Mohti W and Ferreira P 2020 Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak. Int. J. Financial Stud. 8 31 [6] Ausloos M, Grech D, Matteo T Di, Kutner R, Schinckus C and Stanley H E 2020 Econophysics and sociophysics in turbulent world (Phys. A vol 531) ed M Ausloos et al (Amsterdam: Elsevier) [7] Bachelier L 1900 Théorie de la Spéculation Ann. Sci. Ecole Norm 3 21-86 [8] Bandt C 2020 Order patterns, their variation and change points in financial time series and Brownian motion Stat Papers 61 1565-1588 [9] Bandt C and Pompe B 2002 Permutation Entropy: A natural complexity measure for time series Phys. Rev. Lett. 88 174102 [10] Bariviera A F, Zunino L and Rosso O 2016 Crude oil market and geopolitical events: an analysis based on information-theory-based quantifiers Fuzzy Economic Rev. 21 41-51 [11] Barkoulas J T, Chakraborty A and Ouandlous A 2012 A metric and topological analysis of determinism in the crude oil spot market Energy Economics 34 584–91 [12] Barrat A, Barthelemy M and Vespignani A 2008 Dynamical processes on complex networks (UK: Cambridge University Press) [13] Barunik J, Vacha L and Vosvrda M 2010 Tail Behavior of the Central European Stock Markets during the Financial Crisis Czech Economic Rev. 4 281-294 [14] Bautista R Sand Mora J A 2020 Value at risk in the oil sector: an analysis of the efficiency in the measurement of the risk of the α-stable distribution versus the generalized asymmetric Student-t and normal distributions Contaduría y Administración 65 19 [15] Bell S and Morse S 2018 Sustainability Indicators Past and Present:What Next? Sustainability 10 [16] Belov I, Kabašinskas A and Sakalauskas L 2006 A Study of Stable Models of Stock Markets Information Technology and Control 35 ISSN 1392 – 124X [17] Berta M, Christandl M, Colbeck R, Renes J and Renner R 2010 The uncertainty principle in the presence of quantum memory Nature Phys. 6 659-662 [18] Bhaduri S N 2014 Applying approximate entropy (ApEn) to speculative bubble in the stock market J. Emerging Market Finance 13 43–68 [19] Bianconi G 2015 Interdisciplinary and physics challenges in network theory Europhysics Let. 111 56001 [20] Bielinskyi A, Semerikov S, Serdiuk O, Solovieva V, Soloviev V and Pichl L 2020 Econophysics of sustainability indices Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020) (Ukraine: Odessa) vol 2713 (Germany: CEUR Workshop Proceedings) In press [21] Bielinskyi A, Semerikov S, Solovieva V and Soloviev V Levy’s stable distribution for stock crash detecting The 8th Int. Conf. on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019) (SHS Web of Conf. vol 65) ed A Bielinskyi (EDP Sciences, 2019) [22] Bielinskyi A, Soloviev V, Semerikov S and Viktoria Solovieva 2019 Detecting Stock Crashes Using Levy Distribution Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics: Proc. of the Selected Papers of the 8th Int. Conf. on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019) (Ukraine: Odessa) vol 2422 (Germany: CEUR Workshop Proceedings) pp 420-433 [23] Bielinskyi A, Semerikov S, Serdyuk O and Soloviev V 2020 The Econophysics of Cryptocurrency Crashes: An Overview The Int. Conf. on History, Theory and Methodology of Learning (ICHTML 2020) (Urkaine: Kryvyi Rih) (SHS Web of Conferences) In press [24] Blanc J L, Pezard L and Lesne A 2011 Delay independence of mutual-information rate of two symbolic sequences Phys. Rev. E 84 036214 [25] Boccaletti S et al. 2014 The structure and dynamics of multilayer networks Phys. Rep. 544 122 [26] Boccaletti S, Latora V, Moreno Y, Chavez M and Hwang D-U 2006 Complex networks: Structure and dynamics Phys. Rep. 424 175–308 [27] Boltzmann L 1872 Weitere studien uber das wärmegleichgewicht unter gasmolekülen Sitzungsberichte Akademie der Wissenschaften 66 275-370 [28] Bonchev D 2009 Information Theoretic Complexity Measures (Encyclopedia of Complexity and Systems Science vol 1) ed R A Meyers (New York: Springer) chapter 6 pp 4820-4838 [29] Brorsen B W and Yang S R 1990 Maximum likelihood estimates of symmetric stable distribution parameters Communications in Statistics-Simulation and Computation 19 1459-1464 [30] Caraiani P and Haven E 2015 Evidence of multifractality from CEE exchange rates against EuroPhys. A 419 395–407 [31] Chakraborty A, Easwaran S and Sinha S 2016 An “inverse square law” for the currency market: Uncovering hidden universality in heterogeneous complex systems. Available at arXiv:1606.06111[q-fin.ST] [32] ChenS, Carlini M, Krasnogorskaya N andScalia M 2013 Mathematical Problems in Sustainable Energy and Environment Mathematical Problems in Engineering 2013 [33] Cirstea S D, Moldovan-Teselios C, Cirstea A, Turcu A C and Darab C P 2018 Evaluating Renewable Energy Sustainability by Composite Index Sustainability 10 [34] Clausius R and Hirst T 1867 The Mechanical Theory of Heat: With Its Applications to the Steam-engine and to the Physical Properties of Bodies (London: John van Voorst) p 376 [35] Cohen R and Havlin S. 2010 Complex networks. Structure, robustness and function (New York, NY: Cambridge University Press) [36] Colangelo G, Clurana F M, Blanchet L C, Sewell R J and Mitchell M W 2017 Simultaneous tracking of spin angle and amplitude beyond classical limits Nature 543 525-528 [37] Corso G, Prado T, Lima G and Lopes S 2017 A novel entropy recurrence quantification analysis. Available at arXiv:1707.00944v1 [stat.OT] [38] Costa M, Chung-Kang Peng C-K and Goldberger A 2008 Multiscale Analysis of Heart Rate Dynamics: Entropy and Time Irreversibility Measures Cardiovascular Engineering 8 88-93 [39] Costa N, Silva C and Ferreira P 2019 Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies. Int. J. Financ. Stud. 7 51 [40] Da Silva S 2015 Financial market efficiency should be gauged in relative rather than absolute terms J. Stock Forex Trad. 4 140 [41] Da Silva S, Matsushita R and Gigio R 2008 The relative efficiency of stockmarkets Economics Bulletin 7 12 [42] Da Silva S, Taufemback C and Giglio R 2011 Algorithmic complexity theory detects decreases in the relative efficiency of stock markets in the aftermath of the 2008 financial crisis Economics Bulletin 31 1631–47 [43] Da Z, Engelberg J and Gao P 2014 The sum of all FEARS investor sentiment and asset prices Rev. Financ. Stud. 28 [44] Danilchuk H and Soloviev V 2015 Dynamics of graph spectral entropy in financial crisis Socioeconomic aspects of economics and management 2 227–234 [45] Database of Spot Prices (Crude Oil in Dollars per Barrel, Products in Dollars per Gallon). Available at https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm [46] Delbianco F., Tohmé F, Stosic T and Stosic B 2016 Multifractal behavior of commodity markets: Fuel versus non-fuel products Phys. A 457 573– 580. [47] Delgado-Bonal A 2019 Quantifying the randomness of the stock markets Sci Rep 9 12761 [48] Delgado-Bonal A and Marshak A 2019 Approximate Entropy and Sample Entropy: A Comprehensive Tutorial Entropy 21 541 [49] Derbentsev V, Semerikov S, Serdyuk O, Solovieva V and Soloviev V 2020 Recurrence based entropies for sustainability indices The Int. Conf. on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2020) (E3S Web of Conferences vol 166) (EDP Sciences, 2020) p 7 [50] Donner R V, Small M, Donges J. F, Marwan N, Zou Y, Xiang R and Kurths J 2010 Recurrencebased time series analysis by means of complex network methods Int. J. Bifurcation and Chaos 21 1019–46 [51] Duan W-Q and Stanley H 2010 Volatility, irregularity, and predictable degree of accumulative return series Phys. Rev. E 81 066116 [52] Eckmann J P, Kamphorst S O and Ruelle D 1987 Recurrence plots of dynamical systems Europhys. Lett. 5 973–977 [53] Eckmann J-P and Ruelle D 1985 Ergodic theory of chaos and strange attractors Rev. Mod. Phys. 57 617-656 [54] Eom C, Oh G and Jung W S 2008 Relationship between efficiency and predictability in stock price change Phys. A 387 5511–17 [55] Estevez-Rams E, Lora Serrano R, Aragon Fernandez B and Brito Reyes I 2013 On the nonrandomness of maximum lempel ziv complexity sequences of finite size Chaos 23 023118 [56] Fama E F 1965 The behavior of stock market prices J. Business 38 34-105 [57] Fama E F and Roll R 1971 Parameter estimates for symmetric stable distributions J. Am. Stat. Assoc. 66 331-38 [58] Faure P and Korn H 1998 A new method to estimate the Kolmogorov entropy from recurrence plots: its application to neuronal signals Phys. D 122 265–279 [59] Ferreira P and L C Loures 2020 An Econophysics Study of the S&P Global Clean Energy Index Sustainability 12 9 [60] Fukunaga T and Umeno K 2017 Universal Lévy’s stable law of stock market and its characterization. Available at: arXiv:1709.06279 [q-fin.ST] [61] Gabaix X 2009 Power laws in economics and finance Annual Rev. of Economics l 255–294 [62] Gabaix X, Gopikrishnan P, Plerou V and Stanley H E 2003 A theory of power law distributions in financial market fluctuations Nature 423 267-270 [63] Gajardo G, Kristjanpoller W D and Minutolo M 2018 Does bitcoin exhibit the same asymmetric multifractal cross-correlations with crude oil, gold and djia as the euro, great british pound and yen? Chaos, Solitons and Fractals 109 195–205 [64] Ganchuk A, Derbentsev V and Soloviev V N 2006 Multifractal Properties of the Ukraine Stock Market. Available at arXiv:physics/0608009 [physics.data-an] [65] Gang-Jin W, Chi X, Shou C and Feng H 2014 Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis Mathematical Problems in Engineering 2014 13 [66] Gao J and Cai H 2000 On the structures and quantification of recurrence plots Phys. Lett. A 270 75-87 [67] Gibbs J 1914 Elementary principles in statistical mechanics: developed with especial reference to the rational foundation of thermodynamics (Yale University Press) [68] Giglio G and Da Silva S 2009 Ranking the stocks listed on Bovespa according to their relative efficiency University Library of Munich, Germany, MPRA Paper 3 2133–42 [69] Giglio R, Matsushita R, Figueiredo A, Gleria I and Da Silva S 2008 Algorithmic complexity theory and the relative efficiency of financial markets Europhys. Lett. 84 48005 [70] Gnedenko B V and Kolmogorov A N 1954 Limit Distributions for Sums of Independent Random Variables (Addison-Wesley) [71] Gopikrishnan P, Meyer M, Amaral L A N and Stanley H E 1998 Inverse cubic law for the probability distribution of stock price variations The Eur. Phys. J. B 3 139–40 [72] Gopikrishnan P, Plerou V, Amaral L A N, Meyer M and Stanley H E 1999 Scaling of the distribution of fluctuations of financial market indices Phys. Rev. E 60 5305–5316 [73] Gu R 2017 Multiscale Shannon Entropy and its application in the stock market Phys. A 484 215-24 [74] Gu R, Chen H and Wang Y 2010 Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis Phys. A 389 2805–15 [75] Gunay S and Khaki A R 2018 Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models J. Risk Financial Manag. 11 30 [76] Halvin S, and Cohen R 2010 Complex networks. Structure, robustness and function (New York: Cambridge University Press) [77] Hasan R and Mohammad S M 2015 Multifractal analysis of asian markets during 2007-2008 financial crisis Phys. A 419 746-761 [78] Hidalgo E G Quantum Econophysics. arXiv: physics/0609245v1 [physics.soc-ph] [79] Hongli N, Jun W and Cheng L 2018 Analysis of crude oil markets with improved multiscale weighted permutation entropy Phys. A 494 389–402 [80] Hua X, Minggang W and Weiguo Y 2020 Information Linkage between Carbon and Energy Markets: Multiplex Recurrence Network Approach Complexity 2020 12 [81] Huan C, Lixin T, Minggang W and Zaili Z 2017 Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks Sustainability 9 574 [82] Hurst H E 1951 Long term storage capacity of reservoirs ASCE Transactions 116 770-808 [83] Hurst H E 1957 A suggested statistical model of some time series which occur in Nature Nature 180 494 [84] Iacovacci J and Lacasa L 2016 Sequential motif profile of natural visibility graphs Phys. Rev. E 94 052309 [85] International Atomic Energy Agency 2005 Energy Indicators for Sustainable Development: Guidelines and Methodologies (Vienna: IAEA) p 171 [86] Jian W, Wei S and Junseok K 2020 Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures Chaos Solutions Fractals 136 [87] Jiang Z-Q and Zhou W-X 2011 Multifractal detrending moving-average cross-correlation analysis Phys. Rev. E 84 016106 [88] Jiang Z-Q, Xie W-J and Zhou W-X 2014 Testing the weak-form efficiency of the WTI crude oil futures market Phys. A 405 235-244 [89] Joshua S, Richman J and Moorman R 2000 Physiological time-series analysis using approximate entropy and sample entropy Am. J Physiol. Heart Circ. Physiol 278 2039-2049 [90] Kantelhardt J W, Zschiegner S A, Koscielny-Bunde E, Havlin S, Bunde A and Stanley H E 2002 Multifractal detrended fluctuation analysis of nonstationary time series Phys. A 316 87- 114 [91] Kantz H and Schreiber T 2003 Nonlinear Time Series Analysis (2nd ed.). (UK: Cambridge University Press) [92] Kanwal M S, Grochow J A and Ay N 2017 Comparing information-theoretic measures of complexity in Boltzmann machines Entropy 19 310 [93] Kapica J 2012 Entropy analysis of energy price movement Teka. Commission of motorization and energetics in agriculture 12 101–104 [94] Kateregga M, Mataramvura Sand Taylor D | Zhang X (Reviewing Editor) 2017 Parameter estimation for stable distributions with application to commodity futures log-returns Cogent Economics & Finance 5 13118813 [95] Kolmogorov A N 1965 Three approaches to the quantitative definition of information Int. J. Computer Mathematics 2 157-168 [96] Kostanjcar Z and Jeren B 2013 Emergence of Power-Law and Two-Phase Behavior in Financial Market Fluctuations Advances in Complex Systems 16 1350008. [97] Koutrouvelis I A 1980 Regression-type estimation of the parameters of stable laws J. Amer. Statist. Assoc. 75 918-28 [98] Koutrouvelis I A 1981 An iterative procedure for the estimation of the parameters of stable laws Commun. Statist.- Simula. 10 17-28 [99] Krężołek D 2015 The application of alpha-stable distributions in portfolio selection problem - the case of metal market Studia Ekonomiczne 247 56-68 [100] Kristoufek L 2011 Multifractal height cross-correlation analysis: A new method for analyzing long-range cross-correlations Europhys. Lett. 95 68001 [101] Kristoufek L and Vosvrda M 2013 Commodity futures and market efficiency. Available at arXiv:1309.1492v1 [q-fin.ST] [102] Kumar S and Deo N 2009 Multifractal properties of the Indian financial market Phys. A 388 1593-1602 [103] Kuruoğlu E E 2001 Density parameter estimation of skewed α-stable distributions IEEE T.Signal Proces. 49 2192 [104] Kwapień J et al. 2005 Components of multifractality in high-frequency stock returns Phys. A 350 466-474 [105] Lacasa L, Luque B, Ballesteros F, Luque J and Nuño J C 2008 From time series to complex networks: the visibility graph PNAS 105 4972–4975 [106] Lahmiri S 2017 Multifractal analysis of moroccan family business stock returns Phys. A 486 183-191 [107] Lahmiri S and Bekiros S 2020 The impact of covid-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets Chaos, Solitons & Fractals 138 109936 [108] Landau L D and Lifshitis E M 1975 The classical theory of fields. Course of theoretical physics (England: Butterworth-Heinemann/Oxford) [109] Lee J W, Lee K E and Rikvold P A 2006 Multifractal behavior of the Korean stock-market index KOSPI Phys. A 364 355-361 [110] Lempel A and Ziv J 1976 On the complexity of finite sequences IEEE Transactions on Information Theory 22 75–81 [111] Levi P 1924 Théorie des erreurs. La loi de Gauss et les lois exceptionnelles Bulletin de la Société Mathématique de France 52 49-85 [112] Li J, Lu X and Zhou Y 2016 Cross-correlations between crude oil and exchange markets for selected oil rich economies Phys. A 453 131-143 [113] Li Z and Lu X 2011 Multifractal analysis of china’s agricultural commodity futures markets Energy Procedia 5 1920–1926 [114] Lim G, Kim S, Lee H, Kim K and Lee D-I 2007 Multifractal detrended fluctuation analysis of derivative and spot markets Phys. A 386 259–266 [115] Lo A W 1989 Long-term memory in stock market pricesEconometrica 59 1279-1313 [116] Lovász L. 1996 Information and Complexity (How To Measure Them?) (The Emergence of Complexity in Mathematics, Physics, Chemistry and Biology) ed B Pullman (Pontifical Academy of Science/Vatican City/Princeton University Press) pp 65-80 [117] Lu X, Li J, Zhou Y and Qian Y 2017 Cross-correlations between RMB exchange rate and international commodity markets Phys. A 486 168-182 [118] Lux T and Sornette D 2002 On rational bubbles and fat tails J. Money Credit Bank 34 589–610 [119] Ma F, Wei Y and Huang D 2013 Multifractal detrended cross-correlation analysis between the chinese stock market and surrounding stock markets Phys. A 392 1659-1670 [120] Ma F, Wei Y, Huang D and Zhao L 2013 Cross-correlations between west texas intermediate crude oil and the stock markets of the bric Phys. A 392 5356-5368 [121] Ma F, Zhang Q, Peng C and Wei Y 2014 Multifractal detrended cross-correlation analysis of the oil-dependent economies: evidence from the west texas intermediate crude oil and the gcc stock markets Phys. A 410 154-166 [122] Ma X Y and Nikias C L 1995 Parameter estimation and blind channel identification in impulsive signal environments IEEE T. Signal Proces 43 2884-97 [123] Mahmoud I, Naoui K and Jemmali H 2013 Study of speculative bubbles: The contribution of approximate entropy.Int. J. of Econ. and Financial 3 683–693 [124] Malevergne Y, Pisarenko V and Sornette D 2005 Empirical distributions of stock returns: between the stretched exponential and the power law? Quant. Financ. 5 379–401 [125] Malevergne Y, Pisarenko V and Sornette D 2011 Testing the Pareto against the lognormal distributions with the uniformly most powerful unbiased test applied to the distribution of cities Phys. Rev. E 83 036111 [126] Mali P and Mukhopadhyay A 2014 Multifractal characterization of gold market: a multifractal detrended fluctuation analysis Phys. A 413 361–372 [127] Malinetskiy G G 2013 Teoriya samoorganizatsii. na poroge iv paradigmy Computer research and modeling 5 315–366 [in Russian] [128] Mandelbrot B B 1960 The pareto-lévy law and the distribution of income Int. Econ. Rev. 1 79– 106 [129] Mandelbrot B B 1963 The Variation of certain speculative prices The Journal of Business 36 394-419 [130] Mandelbrot B B 1983 The fractal geometry of nature (New York: Freeman) [131] Mantegna R N and Stanley H E 2000 An Introduction to Econophysics: Correlations and Complexity in Finance (Cambridge: Cambridge University Press) [132] Marchuk G I, Aloyan A E and Kondratyev K Ya 2015 Mathematical Models and Simulation in Environment Mathematical models of life support systems 1 10 [133] Marsh P 2012 The New Industrial Revolution: Consumers, Globalization and the End of Mass Production (UK: London/Yale University Press) [134] Martina E, Rodriguez E, Escarela-Perez R and Alvarez-Ramirez J 2011 Multiscale entropy analysis of crude oil price dynamicsEnergy Economics 33 936-947 [135] Marwan N, Romano M C, Theil M and Kurths J 2007 Recurrence plot for the analysis of complex systems Phys. Rep. 438 237–329 [136] Marwan N, Wessel N, Meyerfeldt U, Schirdewan A and Kurths J 2002 Recurrence plot based measures of complexity and its application to heart rate variability data. Phys. Rev. E 66 026702 [137] Maslov V P 2002 Econophysics and quantum statistics Mathematical Notes 72 811-818 [138] Mastroeni L, Vellucci P and Naldi M 2019 A reappraisal of the chaotic paradigm for energy commodity prices Energy Economics 82 167-178 [139] Matia K, Ashkenazy Y and Stanley H E 2003 Multifractal properties of price fluctuations of stocks and commodities Europhys. Lett. 61 422 [140] Matilla-García M 2007 Nonlinear dynamics in energy futures The Energy J. 28 7–29 [141] McCulloch J H 1986 Simple consistent estimators of stable distribution parameters Communication in Statistics - Computation and Simulation 15 1109-36 [142] McNally S, Roche J and Caton S 2018 Predicting the price of bitcoin using machine learning Proc. of the 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) pp 339–343 [143] Mensi W, Tiwari A K and Yoon S-M 2017 Global financial crisis and weak-form efficiency of islamic sectoral stock markets: an mf-dfa analysis Phys. A 471 135–146 [144] Mensi W, Aloui C, Hamdi M and Nguyen D 2012 Crude oil market efficiency: An empirical investigation via the Shannon entropy Économie internationale 129 119-137 [145] Moshiri S and Foroutan F 2006 Forecasting nonlinear crude oil futures prices The Energy J. 27 81–96 [146] Newman M 2003 The structure and function of complex networks SIAM Rev. 45 167–256 [147] Newman M, Watts D and Barabási A-L 2006 The Structure and Dynamics of Networks (Princeton and Oxford: Princeton University Press) [148] Nicolas J M 2002 Introduction aux statistique de deuxi’emeesp’ece: application des logsmoments et des logs-cumulants’ al’analyse des lois d’images radar Trait du Signal 19 139- 167 [149] Nolan J P 2001 Maximum likelihood estimation of stable parameters (Lévy Processes: Theory and applications), ed O E Barndorff-Nielsen, S I Resnick and T Mikosch (Birkhäuser, Boston, MA) pp 379-400 [150] Nolan J P 2003 Modeling Financial Data with Stable Distributions Handbooks in Finance (Handbook of Heavy Tailed Distributions in Finance vol 1) ed T Svetlozar (North Holland: Elsevier) chapter 3 pp 105-130 [151] Norouzzadeh P and Rahmani B 2006 A multifractal detrended fluctuation description of Iranian rial–US dollar exchange rate Phys. A 367 328–336 [152] Norouzzadeh P, Dullaert W and Rahmani B 2007 Anti-correlation and multifractal features of spain electricity spot market Phys. A 380 333–342 [153] Oh G, Eom C, Havlin S, Jung W-S, Wang F, Stanley H and Kim S 2012 A multifractal analysis of asian foreign exchange markets The European Phys. J. B 85 214 [154] Ott E, Sauer T and Yorke J A 1994 Coping with chaos (New York: Wiley Interscience) [155] Peng C-K, Buldyrev S V, Havlin S, Simons M, Stanley H E and Goldberger A L 1994 Mosaic organization of dna nucleotides Phys. Rev. E 49 1685-89 [156] Pincus S and Kalman R E 2004 Irregularity, volatility, risk, and financial market time series. Proc. of the National Academy of Sciences 101 13709-714 [157] Pincus S M 1991 Approximate entropy as a measure of system complexity Proc. Natl. Acad. Sci. USA 88 2297-2301 [158] Pincus S M and Goldberger A L 1994 Physiological time-series analysis: what does regularity quantify? American Journal of Physiology-Heart and Circulatory Physiology 266 H1643– H1656 [159] Podobnik B and Stanley H E 2008 Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series Phys. Rev. Let. 100 084102 [160] Podobnik B, Horvatic D, Petersen A M and Stanley H E 2009 Cross-correlations between volume change and price change Proc. of the National Academy of Sciences of the United States of America 106 22079–084 [161] Podobnik B, Matia K, Chessa A, Ivanov P C, Lee Y and Stanley H E 2001 Time evolution of stochastic processes with correlations in the variance: stability in power-law tails of distributions Phys. A 300 300-9 [162] Podobnik B, Valentincic A, Horvatic D and Stanley H E 2011 Asymmetric Lévy flight in financial ratios PNAS 108 17883-88 [163] Poincaré H 1890 Sur le problème des trois corps et les équations de la dynamique. Acta mathematica 13 270 [164] Prevedel R, Hamel D R, Colbeck R, Fisher K and Resch K J 2011 Experimental investigation of the uncertainty principle in the presence of quantum memory Nature Phys.7 757-761 [165] Pueyo S 2014 Ecological Econophysics for Degrowth Sustainability 6 3431-83 [166] Qian M C, Jiang Z Q and Zhou W X 2010 Universal and nonuniversal allometric scaling behaviors in the visibility graphs of world stock market indices Phys. A 43 335002 [167] Qian X-Y, Liu Y-M, Jiang Z-Q, Podobnik B, Zhou W-X and Stanley H E 2015 Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces Phys. Rev. E 91 062816 [168] Qin J, Lu X, Zhou Y and Qu L 2015 The effectiveness of china’s rmb exchange rate reforms: an insight from multifractal detrended fluctuation analysis Phys. A 421 443–454 [169] Razmjoo A A and Sumper A 2019 Investigating energy sustainability indicators for developing countries Int. J. Sustainable Energy Planning and Management 21 59-76 [170] Razmjoo A A, Sumper A and Davarpanah A 2019 Development of sustainable energy indexes by the utilization of newindicators: A comparative study Energy Rep. 5 375-383 [171] Rodriguez E B and Aguilar L M A 2018 Disturbance-disturbance uncertainty relation: the statistical distinguishability of quantum states determines disturbance Scientific Reports 8 4010 [172] Rongbao G, Hongtao C and Yudong W 2010 Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis Phys. A 389 2805-15 [173] Rozema L A, Darabi A, Mahler D H, Hayat A, Soudagar Y and Steinberg A M 2012 Violation of heisenberg’s measurement-disturbance relationship by weak measurements Phys. Rev. Lett. 109 100404 [174] Saptsin V and Soloviev V 2009 Relativistic quantum econophysics - new paradigms in complex systems modelling. Available at: arXiv:0907.1142v1 [physics.soc-ph] [175] Sattarhoff C and Gronwald M 2018 How to measure financial market efficiency? a multifractality-based quantitative approach with an application to the european carbon market Discussion Paper in Economics 18 ISSN0143-4543 [176] Shannon C E 1948 A mathematical theory of communication The Bell System Technical J. 27 379-423 [177] Shao M and Nikias C L P 1993 Signal processing with fractional lower order moments: stable processes and their applications Proc. of the IEEE 81 986-1010 [178] Shaobo H, Chunbiao L, Kehui S and Sajad J 2018 Multivariate Multiscale Complexity Analysis of Self-Reproducing Chaotic Systems Entropy 20 556 [179] Shaohui Z and Tian Z 2019 Multifractal Detrended Cross-Correlation Analysis of Electricity and Carbon Markets in China Mathematical Problems in Engineering 2019 13 [180] Shuen-De W, Chiu-Wen W, Shiou-Gwo L, Kung-Yen L and Chung-Kang Peng 2014 Analysis of complex time series using refined composite multiscale entropyPhys. Lett. A 378 1369-74 [181] Siegenfild A F and Bar-Yam Y 2020 An Introduction to Complex Systems Science and Its Applications Complexity 2020 16 [182] Sole R V and Valverde S 2004 Information theory of complex networks: on Evolution and Architectural constraints Lecture Notes in Phys. 650 189-207 [183] Soloviev V and Belinskij A 2018 Complex systems theory and crashes of cryptocurrency market. Information and Communication Technologies in Education, Research, and Industrial (Communications in Computer and Information Science 2019 vol 1007) ed V Ermolayev et al (Cham: Springer) pp 276-297 [184] Soloviev V and Belinskij A 2018 Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors Proc. of the 14th Int. Conf. on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops (Ukraine: Kyiv) vol 2104 (Germany: CEUR Workshop Proceedings) pp 116–127 [185] Soloviev V and Saptsin V 2011 Heisenberg uncertainty principle and economic analogues of basic physical quantities. Available at arXiv:1111.5289v1 [physics.gen-ph] [186] Soloviev V and Serdiuk O 2020 Quantum econophysical precursors of cryptocurrency crashes Cherkasy University Bulletin: Applied Mathematics. Informatics 1 3-16 [187] Soloviev V N and Romanenko Y V 2017 Economic analog of heisenberg uncertainty principle and financial crisis. Proc. in 20th Int. Conf. SAIT 2017 (Ukraine: Igor Sikorsky Kyiv Polytechnic Institute) (ESC “IASA” NTUU) pp 32-33 [188] Soloviev V N, Bielinskyi A, Serdyuk O, Solovieva V and Semerikov S 2020 Lyapunov Exponents as Indicators of the Stock Market Crashes Proc. of 16th Int. Conf. on ICT in Research, Education and Industrial Applications (ICTERI 2020) (Workshops 2020 vol 2) ed Zholtkevych G, Sokolov O et al. (Germany: CEUR Workshop Proceedings) p 16 In press [189] Soloviev V N, Yevtushenko S P and Batareyev V V 2019 Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory Computer Science & Software Engineering: Proc. of the 2nd Student Workshop(Ukraine: Krivyi Rih) vol 2546 (Germany: CEUR Workshop Proceedings) pp 87-100 [190] Soloviev V, Belinskij A and Solovieva V 2019 Entropy analysis of crisis phenomena for DJIA index. Proc. of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Vol. II: Workshops (Ukraine: Kherson) vol 2393 (Germany: CEUR Workshop Proceedings) pp 434– 449 [191] Soloviev V, Semerikov S and Solovieva V 2020 Lempel-ziv complexity and crises of cryptocurrency market III International Scientific Congress Society of Ambient Intelligence 2020 (Advances in Economics, Business and Management Research vol 129) (Atlantis Press) pp 299-306 [192] Soloviev V, Serdiuk O, Semerikov S and Kiv A 2020 Recurrence plot-based analysis of financialeconomic crashes Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020) (Ukraine: Odessa) vol 2713 (Germany: CEUR Workshop Proceedings) In press [193] Soloviev V, Serdiuk O, Semerikov S and Kohut-Ferens O 2019 Recurrence entropy and financial crashes Proc. of the 2019 7th Int. Conf. on Modeling, Development and Strategic Management of Economic System (MDSMES 2019) (Advances in Economics, Business and Management Research vol 99) (Atlantis Press) pp 385-88 [194] Soloviev V, Solovieva V and Tuliakova A 2019 Visibility graphs and precursors of stock crashes Neuro-Fuzzy Technologies of Modeling in Economy 8 3–29 [195] Soloviev V, Solovieva V, Tuliakova A, Hostryk A and Pichl L 2020 Complex networks theory and precursors of financial crashes Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020) (Ukraine: Odessa) vol 2713 (Germany: CEUR Workshop Proceedings) In press [196] Soloviev V and Tuliakova A 2016 Graphodinamical Research Methods for Complexity of Modern Stock Markets Neuro-Fuzzy Technologies of Modeling in Economy 5 152–179 [197] Sornette D 2003 Why Stock Markets Crash (USA: Princeton/Princeton University Press) [198] Soyyiğit S, Topuz H and Halil Ö2020 An Alternative View to the Global Coal Trade: Complex Network Approach Studies in Business and Economics 15 270-288 [199] Suárez-García P and Gómez-Ullate D 2014 Multifractality and long memory of a financial index Phys. A 394 226-34 [200] Suganthi L 2020 Sustainability indices for energy utilizationusing a multi-criteria decision model Sustainability and Society 10 [201] Takens F 1981 Detecting strange attractors in turbulence Dynamical Systems and Turbulence, Lecture Notes in Mathematics 898 366–81 [202] Taleb N N 2020 On the statistical differences between binary forecasts and real world payoffs. Available at arXiv:1907.11162 [q-fin.GN] [203] TalebN N, Bar-Yam Y and Cirillo P 2020 On single point forecasts for fat-tailed variables. Available at arXiv:2007.16096 [physics.soc-ph] [204] Tang Y, Kurths J, Lin W, Ott E and Kocarev L 2020 Introduction to Focus Issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics Chaos 30 063151 [205] Thiel M, Romano M C and Kurths J 2003 Analytical description of recurrence plots of white noise and chaotic processes Appl. Nonlinear Dyn. 11 20–30 [206] Thiel M, Romano M C, Kurths J, Meucci R, Allaria E and Arecchi F T 2002 Influence of observational noise on the recurrence quantification analysis Phys. D 171 138–152 [207] Thurner S, Hanel R and Klimek P 2018 Introduction to the Theory of Complex Systems (UK: Oxford/Oxford Univ.press) p 448 [208] Ting L L et al. 2015 Complexity Measurement Based on Information Theory and Kolmogorov Complexity Artificial Life 21 205-24 [209] Tiwari A, Albulescu C and Yoon S 2017 A multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices Phys. A 483 182–92 [210] Tsallis C 2009 Introduction to Nonextensive Statistical Mechanics, Approaching a Complex World (New-York: Springer) [211] Tung-Li Shih and Hai-Chin Yu Probability Distribution of Return and Volatility in Crude Oil Market Available at http://www.jgbm.org/page/21%20Tung-Li%20Shih%20.pdf [212] Umeno K 2016 Ergodic transformations on r preserving cauchy laws Nonlinear Theory and Its Applications 7 14-20 [213] Wang D-H, Yu X-W and Suo Y-Y 2012 Statistical properties of the yuan exchange rate index Phys. A 391 3503-12 [214] Wang J, Shang P and Ge W 2011 Multifractal cross-correlation analysis based on statistical moments Fractals 20 271-9 [215] Wang Y, Wei Y and Wu C 2010 Cross-correlations between chinese a-share and b-share markets Phys. A 389 5468-78 [216] Wang Y, Wei Y and Wu C 2011 Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis Phys. A 390 817-27 [217] Wang Y, Wei Y and Wu C 2011 Detrended fluctuation analysis on spot and futures markets of west texas intermediate crude oil Phys. A 390 864-75 [218] Wa̧torek M, Drożdż S, Oświȩcimka P and Stanuszek M 2019 Multifractal cross-correlations between the world oil and other financial markets in 2012–2017 Energy Economics 81 874- 885 [219] Webber Jr C L and Zbilut J P 1994 Dynamical assessment of physiological systems and states using recurrence plot strategies J. Appl. Physiol. 76 965-73 [220] Webber Jr C L and Zbilut J P 2005 Recurrence quantification analysis of nonlinear dynamical systems Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences 26-94 [221] Webber Jr C L, Ioana C and Marwan N 2016 Recurrence plots and their quantifications: expanding horizons 2016 Proc. of the 6th Int. Symposium on Recurrence Plots (France: Grenoble) vol 180 (Heidelberg: Springer) p 387 [222] Webber Jr C L, Ioana C and Marwan N 2016 Recurrence plots and their quantifications: expanding horizons 2016 Proc. of the 6th Int. Symposium on Recurrence Plots (France: Grenoble) vol. 180 (Heidelberg: Springer) p 387 [223] Wei-Shing C and Sheng-Yu C 2012 Application of Permutation Entropy and Statistical Complexity Measure in Crude Oil Price Time Series. Available at http://cyc2012.dyu.edu.tw/pdf/D-5- 利用排列熵與統計複雜量測於原油價格時間序列之研究(陳偉星).pdf [224] World Energy Council 2020 World Energy Trilemma Index Available at https://www.worldenergy.org/assets/downloads/World_Energy_Trilemma_Index_2020_- _REPORT.pdf [225] Wu J and T Wu 2012 Sustainability indicators and indices: an Overview (Handbook of Sustainable Management) ed C N Madu and C Kuei (London: Imperial College Press) chapter 4 pp 65-86 [226] Xie C, Zhou Y, Wang G and Yan X 2017 Analyzing the cross-correlation between onshore and offshore rmb exchange rates based on multifractal detrended cross-correlation analysis (MFDCCA) Fluctuation and Noise Letters 16 1750004 [227] Yali Z and Jun W 2017 Nonlinear complexity of random visibility graph and Lempel-Ziv on multitype range-intensity interacting financial dynamics Phys. A 482 741-756 [228] Yi Y and Pengjian S 2014 Weighted multiscale permutation entropy of financial time series Nonlinear Dynamics 78 2921-2939 [229] Yingchao Z, Lean Y and Kaijian H 2015 Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics Entropy 17 7167-7184 [230] Yue P, Xu H-C, Chen W, Xiong X and Zhou W-X 2017 Linear and nonlinear correlations in the order aggressiveness of chinese stocks Fractals 25 1750041 [231] Yun K et al. 2012 Decreased cortical complexity in methamphetamine abusers Psychiatry Research: Neuroimaging 201 226–232 [232] Zanin M, Zunino L, Rosso O and Papo D 2012 Permutation entropy and its main biomedcal and econophysics applications: a review Entropy 14 1553 [233] Zbilut J P and Webber C L Jr 1992 Embeddings and delays as derived from quantification of recurrence plots Phys. Lett. A 171 199–203 [234] Zhang W, Wang P, Li X and Shen D 2018 Twitter’s daily happiness sentiment and international stock returns: evidence from linear and nonlinear causality tests J. Behavioral and Experimental Finance 18 50-3 [235] Zhang Z, Zhang Y, Shen D and Zhang W 2018 The cross-correlations between onlinesentiment proxies: evidence from google trends and twitter Phys. A 508 67-75 [236] Zhang Z, Zhang Y, Shen D and Zhang W 2018 The dynamic cross-correlations between mass media news, new media news, and stock returns Complexity 2018 [237] Zheng S and Lan X 2016 Multifractal analysis of spot rates in tanker markets and their comparisons with crude oil markets Phys. A 444 547–59 [238] Zhi-Qiang J, Wen-Jie X and Wei-Xing Z 2012 Testing the weak-form efficiency of the WTI crude oil futures market. Available at arXiv:1211.4686v1 [q-fin.ST] 20 Nov 2012 [239] Zhou W-X 2008 Multifractal detrended cross-correlation analysis for two nonstationary signals Phys. Rev. E 77 066211 [240] Zhuang X, Wei Y and Ma F 2015 Multifractality, efficiency analysis of chinese stock market and its cross-correlation with wti crude oil price Phys. A430(C) 101-13 [241] Zhuang X, Wei Y and Zhang B 2014 Multifractal detrended cross-correlation analysis of carbon and crude oil markets Phys. A 399 113-125 [242] Zou Y, Donner R V, Marwan N, Donges J F and Kurths J 2019 Complex network approaches to nonlinear time series analysis Phys. Rep. 787 97 [243] Zozor S, Ravier P and Buttelli O 2005 On lempel-ziv complexity for multidimensional data analysis Phys. A 345 285–302 [244] Zunino L, Figliola A, Tabak B M, Pérez D G, Garavaglia M and Rosso O A 2009 Multifractal structure in latin-american market indices Chaos, Solitons & Fractals 41 2331-2340

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