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A Novel Integrated Measure for Energy Market Efficiency

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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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References

  1. 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.

    Google Scholar 

  2. 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.

    Google Scholar 

  3. 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.

    Google Scholar 

  4. Malkiel B, The efficient market hypothesis and its critics, Journal of Economic Perspectives, 2014, 17(1): 59–82.

    Google Scholar 

  5. Kristoufek L and Vosvrda M, Commodity futures and market efficiency, Energy Economics, 2014, 42: 50–57.

    Google Scholar 

  6. 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.

    Google Scholar 

  7. Fama E F, The behavior of stock-market prices, Journal of Business, 1965, 38(1): 34–105.

    Google Scholar 

  8. Samuelson P A, Proof that properly anticipated prices fluctuate randomly, Industrial Management Review, 1965, 6(2): 41–49.

    Google Scholar 

  9. 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.

    Google Scholar 

  10. 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.

    Google Scholar 

  11. 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.

    Google Scholar 

  12. Lahmiri S and Bekiros S, Chaos, randomness and multi-fractality in Bitcoin market, Chaos, Solitons & Fractals, 2018, 106: 28–34.

    MathSciNet  MATH  Google Scholar 

  13. Song C, Havlin S, and Makse H A, Self-similarity of complex networks, Nature, 2005, 433(7024): 392–395.

    Google Scholar 

  14. Hurst H E, Long-term storage capacity of reservoirs, Transactions of the American Society of Civil Engineers, 1951, 116(776): 770–808.

    Google Scholar 

  15. 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.

    Google Scholar 

  16. Peng C K, Buldyrev S V, Havlin S, et al., Mosaic organization of DNA nucleotides, Physical Review E, 2005, 49(2): 1685–1689.

    Google Scholar 

  17. Kristoufek L, Are the crude oil markets really becoming more efficient over time? Some new evidence, Working Papers IES, 2018.

  18. 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.

    MATH  Google Scholar 

  19. 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.

    Google Scholar 

  20. 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.

    Google Scholar 

  21. 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.

    Google Scholar 

  22. Lipsitz L A and Goldberger A L, Loss of complexity and aging, Jama, 1992, 267(13): 1806–1809.

    Google Scholar 

  23. 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.

    MathSciNet  MATH  Google Scholar 

  24. Theiler J, Estimating fractal dimension, Journal of the Optical Society of America A, 1990, 7(6): 1055–1073.

    MathSciNet  Google Scholar 

  25. Grassberger P and Procaccia I, Estimation of the Kolmogorov entropy from a chaotic signal, Physical Review A, 1983, 7(6): 2591–2593.

    Google Scholar 

  26. Pyragas K, Continuous control of chaos by self-controlling feedback, Physics Letters A, 1992, 170(6): 421–428.

    Google Scholar 

  27. Eckmann J P, Kamphorst S O, and Ruelle D, Recurrence plots of dynamical systems, Europhysics Letters, 1987, 4(9): 973–977.

    Google Scholar 

  28. Adrangi B, Chatrath A, Dhanda K K, et al., Chaos in oil prices? Evidence from futures markets, Energy Economics, 2001, 23(4): 405–425.

    Google Scholar 

  29. 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.

    Google Scholar 

  30. 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.

  31. Martina E, Rodriguez E, Escarela-Perez R, et al., Multiscale entropy analysis of crude oil price dynamics, Energy Economics, 2011, 33(5): 936–947.

    Google Scholar 

  32. 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.

    Google Scholar 

  33. Yin J, Su C, Zhang Y, et al., Complexity analysis of carbon market using the modified multi-scale entropy, Entropy, 2018, 20(6): 434.

    Google Scholar 

  34. 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.

    Google Scholar 

  35. 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.

    MathSciNet  MATH  Google Scholar 

  36. Sauer T, Yorke J A, and Casdagli M, Embedology, Journal of Statistical Physics, 1991, 65(3–4): 579–616.

    MathSciNet  MATH  Google Scholar 

  37. 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.

    MathSciNet  Google Scholar 

  38. 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.

    Google Scholar 

  39. Journel A G and Deutsch C V, Entropy and spatial disorder, Mathematical Geology, 1993, 25(3): 329–355.

    Google Scholar 

  40. Pincus S M, Approximate entropy as a measure of system complexity, Proceedings of the National Academy of Sciences, 1991, 88(6): 2297–2301.

    MathSciNet  MATH  Google Scholar 

  41. 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.

    Google Scholar 

  42. 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.

  43. Frances A, Kahn D, Carpenter D, et al., A new method of developing expert consensus practice, Am. J. Man Care., 1998, 4: 1023–1029.

    Google Scholar 

  44. 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.

    Google Scholar 

  45. Xu Y, Modeling Risk Management for Resources and Environment in China, Springer, Berlin, 2011.

    Google Scholar 

  46. 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.

    MATH  Google Scholar 

  47. 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.

    Google Scholar 

  48. Zhu B, Wang P, Chevallier J, et al., Carbon price analysis using empirical mode decomposition, Computational Economics, 2015, 45(2): 195–206.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, 100191, China

    Ling Tang

  2. School of Science, Beijing University of Chemical Technology, Beijing, 100029, China

    Huiling Lü & Fengmei Yang

  3. School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Lean Yu

  4. School of Economics, Capital University of Economics and Business, Beijing, 100070, China

    Jingjing Li

Authors
  1. Ling Tang
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  2. Huiling Lü
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  3. Fengmei Yang
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  4. Lean Yu
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  5. Jingjing Li
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Corresponding author

Correspondence to Jingjing Li.

Additional information

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

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