Abstract
This paper formulates a novel integrated measure for energy market efficiency, by investigating different aspects of the market performance. Different from most existing models focusing on one certain aspect, the novel measure especially takes into consideration the self-similarity (or system memo ability or long-term persistence) via fractality, the attractor properties in phase-space via chaos, and disorder state of data dynamics via entropy. In the proposed method, the most popular data analysis techniques of multi-fractal detrended fluctuation analysis, correlation dimension, and sample entropy are respectively conducted on the market returns to capture the corresponding features, and the entropy weight method is then used to generate the final integrated index. For illustration and verification, the proposed measure is applied to three typical energy markets analyses. The empirical results find that natural gas market and crude oil market are much more efficient than carbon market.
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Tang L, Dai W, Yu L, et al., A novel CEEMD-based EELM ensemble learning paradigm for crude oil price forecasting, International Journal of Information Technology and Decision Making, 2015, 14(1): 141–169.
Tang L, Wu J, Yu L, et al., Carbon emissions trading scheme exploration in China: A multi-agent-based model, Energy Policy, 2015, 81: 152–169.
Yu L, Zhao Y, and Tang L, A compressed sensing based AI learning paradigm for crude oil price forecasting, Energy Economics, 2014, 46: 236–245.
Malkiel B, The efficient market hypothesis and its critics, Journal of Economic Perspectives, 2014, 17(1): 59–82.
Kristoufek L and Vosvrda M, Commodity futures and market efficiency, Energy Economics, 2014, 42: 50–57.
Kristoufek L and Vosvrda M, Measuring capital market efficiency: Global and local correlations structure, Physica A: Statistical Mechanics and Its Applications, 2013, 392(1): 184–193.
Fama E F, The behavior of stock-market prices, Journal of Business, 1965, 38(1): 34–105.
Samuelson P A, Proof that properly anticipated prices fluctuate randomly, Industrial Management Review, 1965, 6(2): 41–49.
Tang L, Yu L, and He K J, A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting, Applied Energy, 2014, 128: 1–14.
Tang L, Yu L, Liu F, et al., An integrated data characteristic testing scheme for complex time series data exploration, International Journal of Information Technology and Decision Making, 2013, 12(3): 491–521.
Tang L, Wang C, and Wang S, Energy time series data analysis based on a novel integrated data characteristic testing approach, Procedia Computer Science, 2013, 17: 759–769.
Lahmiri S and Bekiros S, Chaos, randomness and multi-fractality in Bitcoin market, Chaos, Solitons & Fractals, 2018, 106: 28–34.
Song C, Havlin S, and Makse H A, Self-similarity of complex networks, Nature, 2005, 433(7024): 392–395.
Hurst H E, Long-term storage capacity of reservoirs, Transactions of the American Society of Civil Engineers, 1951, 116(776): 770–808.
Kiyono K, Struzik Z R, Aoyagi N, et al., Phase transition in a healthy human heart rate, Physical Review Letters, 2005, 95(5): 58–101.
Peng C K, Buldyrev S V, Havlin S, et al., Mosaic organization of DNA nucleotides, Physical Review E, 2005, 49(2): 1685–1689.
Kristoufek L, Are the crude oil markets really becoming more efficient over time? Some new evidence, Working Papers IES, 2018.
Kantelhardt J W, Zschiegner S A, Koscielny-Bunde E, et al., Multifractal detrended fluctuation analysis of nonstationary time series, Physica A: Statistical Mechanics and Its Applications, 2002, 316(1–4): 87–114.
Wang Y and Liu L, Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis, Energy Economics, 2010, 32(5): 987–992.
Gu R, Chen H, and Wang Y, Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis, Physica A: Statistical Mechanics and Its Applications, 2010, 389(14): 2805–2815.
Fan X, Lu X, Yin J, et al., Quantifying market efficiency of China’s regional carbon market by multifractal detrended analysis, Energy Procedia, 2018, 152: 787–792.
Lipsitz L A and Goldberger A L, Loss of complexity and aging, Jama, 1992, 267(13): 1806–1809.
Wolf A, Swift J B, Swinney H L, et al., Determining Lyapunov exponents from a time series, Physica D: Nonlinear Phenomena, 1985, 16(3): 285–317.
Theiler J, Estimating fractal dimension, Journal of the Optical Society of America A, 1990, 7(6): 1055–1073.
Grassberger P and Procaccia I, Estimation of the Kolmogorov entropy from a chaotic signal, Physical Review A, 1983, 7(6): 2591–2593.
Pyragas K, Continuous control of chaos by self-controlling feedback, Physics Letters A, 1992, 170(6): 421–428.
Eckmann J P, Kamphorst S O, and Ruelle D, Recurrence plots of dynamical systems, Europhysics Letters, 1987, 4(9): 973–977.
Adrangi B, Chatrath A, Dhanda K K, et al., Chaos in oil prices? Evidence from futures markets, Energy Economics, 2001, 23(4): 405–425.
Barkoulas J T, Chakraborty A, and Ouandlous A, A metric and topological analysis of determinism in the crude oil spot market, Energy Economics, 2012, 34(2): 584–591.
Papaioannou G P, Dikaiakos C, Dramountanis A, et al., Using nonlinear stochastic and deterministic (chaotic tools) to test the EMH of two Electricity Markets the case of Italy and Greece, arXiv preprint arXiv: 1711.10552, 2017.
Martina E, Rodriguez E, Escarela-Perez R, et al., Multiscale entropy analysis of crude oil price dynamics, Energy Economics, 2011, 33(5): 936–947.
Ortiz-Cruz A, Rodriguez E, Ibarra-Valdez C, et al., Efficiency of crude oil markets: Evidences from informational entropy analysis, Energy Policy, 2012, 41: 365–373.
Yin J, Su C, Zhang Y, et al., Complexity analysis of carbon market using the modified multi-scale entropy, Entropy, 2018, 20(6): 434.
Tang L, Lü H, and Yu L, An EEMD-based multi-scale fuzzy entropy approach for complexity analysis in clean energy markets, Applied Soft Computing, 2017, 56: 124–133.
Tang L, Wang S, He K, et al., A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting, Annals of Operations Research, 2015, 234(1): 111–132.
Sauer T, Yorke J A, and Casdagli M, Embedology, Journal of Statistical Physics, 1991, 65(3–4): 579–616.
Rosenstein M T, Collins J J, and De Luca C J, Reconstruction expansion as a geometry-based framework for choosing proper delay times, Physica D: Nonlinear Phenomena, 1994, 73(1–2): 82–98.
Kennel M B, Brown R, and Abarbanel H D I, Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A, 1992, 45(6): 3403.
Journel A G and Deutsch C V, Entropy and spatial disorder, Mathematical Geology, 1993, 25(3): 329–355.
Pincus S M, Approximate entropy as a measure of system complexity, Proceedings of the National Academy of Sciences, 1991, 88(6): 2297–2301.
Li X, Wang K, Liu L, et al., Application of the entropy weight and TOPSIS method in safety evaluation of coal mines, Procedia Engineering, 2011, 26: 2085–2091.
Fatemi F, Ardalan A, Aguirre B, et al., Constructing the indicators of assessing human vulnerability to industrial chemical accidents: A consensus-based fuzzy Delphi and fuzzy AHP approach, PLoS Currents, 2017, 9.
Frances A, Kahn D, Carpenter D, et al., A new method of developing expert consensus practice, Am. J. Man Care., 1998, 4: 1023–1029.
Kilincci O and Onal S A, Fuzzy AHP approach for supplier selection in a washing machine company, Expert Systems with Applications, 2011, 38(8): 9656–9664.
Xu Y, Modeling Risk Management for Resources and Environment in China, Springer, Berlin, 2011.
Kantelhardt J W, Zschiegner S A, Koscielny-Bunde E, et al., Multifractal detrended fluctuation analysis of nonstationary time series, Physica A: Statistical Mechanics and Its Applications, 2002, 316(1–4): 87–114.
Zhang X, Lai K K, and Wang S Y, A new approach for crude oil price analysis based on empirical mode decomposition, Energy Economics, 2008, 30(3): 905–918.
Zhu B, Wang P, Chevallier J, et al., Carbon price analysis using empirical mode decomposition, Computational Economics, 2015, 45(2): 195–206.
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This work was supported by the Major Program of the National Fund of Philosophy and Social Science of China under Grant No. 18ZDA106.
This paper was recommended for publication by Editor WANG Shouyang.
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Tang, L., Lü, H., Yang, F. et al. A Novel Integrated Measure for Energy Market Efficiency. J Syst Sci Complex 33, 1108–1125 (2020). https://doi.org/10.1007/s11424-020-8328-4
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DOI: https://doi.org/10.1007/s11424-020-8328-4