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Testing the Efficiency of Electricity Markets Using a New Composite Measure Based on Nonlinear TS Tools

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  • George P. Papaioannou

    (Research, Technology & Development Department, Independent Power Transmission Operator (IPTO) S.A., 89 Dyrrachiou & Kifisou Str. Gr, 104 43 Athens, Greece
    Center for Research and Applications in Nonlinear Systems (CRANS), Department of Mathematics, University of Patras, 26 500 Patras, Greece
    These authors contributed equally to this work.)

  • Christos Dikaiakos

    (Research, Technology & Development Department, Independent Power Transmission Operator (IPTO) S.A., 89 Dyrrachiou & Kifisou Str. Gr, 104 43 Athens, Greece
    Department of Electrical and Computer Engineering, University of Patras, 26 500 Patras, Greece
    These authors contributed equally to this work.)

  • Akylas C. Stratigakos

    (Department of Electrical and Computer Engineering, University of Patras, 26 500 Patras, Greece
    These authors contributed equally to this work.)

  • Panos C. Papageorgiou

    (Department of Electrical and Computer Engineering, University of Patras, 26 500 Patras, Greece
    These authors contributed equally to this work.)

  • Konstantinos F. Krommydas

    (Department of Electrical and Computer Engineering, University of Patras, 26 500 Patras, Greece
    These authors contributed equally to this work.)

Abstract

In this paper we examine and compare the efficiency of four European electricity markets (NordPool, Italian, Spanish and Greek) of different microstructure and level of maturity, by testing the weak form of the Efficient Market Hypothesis (EMH). To quantify the level of efficiency deviation of each market from the ‘ideal’ or ‘benchmark market of random walk’, we have constructed a Composite Electricity Market Efficiency Index (EMEI), inspired by similar works on other energy commodities. The proposed index consists of linear and nonlinear components each one measuring a different feature or dimension of the market efficiency such as its complexity, fractality, entropy, long-term memory or correlation, all connected to the associated benchmark values of the Random Walk Process (RWP). The key findings are that overall, all examined electricity markets are inefficient in respect to the weak form of EMH and the less inefficient market, as measured by the EMEI is the NordPool, closely followed by the Spanish market, with the Italian being the third. The most inefficient market is the Greek one. These results are in accordance with the predominant view about the maturity of these markets. This study contributes significantly on improving the research framework in developing consistent and robust tools for efficiency measurement, while the proposed index can be a valuable tool in designing improved guidelines towards enhancing the efficiency of electricity markets.

Suggested Citation

  • George P. Papaioannou & Christos Dikaiakos & Akylas C. Stratigakos & Panos C. Papageorgiou & Konstantinos F. Krommydas, 2019. "Testing the Efficiency of Electricity Markets Using a New Composite Measure Based on Nonlinear TS Tools," Energies, MDPI, vol. 12(4), pages 1-30, February.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:618-:d:206208
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    as
    1. Narayan, Paresh Kumar & Narayan, Seema & Zheng, Xinwei, 2010. "Gold and oil futures markets: Are markets efficient?," Applied Energy, Elsevier, vol. 87(10), pages 3299-3303, October.
    2. Charles, Amélie & Darné, Olivier, 2009. "The efficiency of the crude oil markets: Evidence from variance ratio tests," Energy Policy, Elsevier, vol. 37(11), pages 4267-4272, November.
    3. Hooi Hooi Lean & Michael McAleer & Wing-Keung Wong, 2010. "Market Efficiency of Oil Spot and Futures: A Stochastic Dominance Approach," CIRJE F-Series CIRJE-F-705, CIRJE, Faculty of Economics, University of Tokyo.
    4. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    5. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    6. Alvarez-Ramirez, Jose & Alvarez, Jesus & Solis, Ricardo, 2010. "Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern," Energy Economics, Elsevier, vol. 32(5), pages 993-1000, September.
    7. Lean, Hooi Hooi & McAleer, Michael & Wong, Wing-Keung, 2010. "Market efficiency of oil spot and futures: A mean-variance and stochastic dominance approach," Energy Economics, Elsevier, vol. 32(5), pages 979-986, September.
    8. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
    9. Kristoufek, Ladislav & Vosvrda, Miloslav, 2013. "Measuring capital market efficiency: Global and local correlations structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 184-193.
    10. Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021. "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, vol. 93(C).
    11. Nomikos, Nikos & Andriosopoulos, Kostas, 2012. "Modelling energy spot prices: Empirical evidence from NYMEX," Energy Economics, Elsevier, vol. 34(4), pages 1153-1169.
    12. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2011. "Detrended fluctuation analysis on spot and futures markets of West Texas Intermediate crude oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 864-875.
    13. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    14. Arciniegas, Ismael & Barrett, Chris & Marathe, Achla, 2003. "Assessing the efficiency of US electricity markets," Utilities Policy, Elsevier, vol. 11(2), pages 75-86, June.
    15. Papaioannou, George P. & Dikaiakos, Christos & Dagoumas, Athanasios S. & Dramountanis, Anargyros & Papaioannou, Panagiotis G., 2018. "Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model," Energy, Elsevier, vol. 142(C), pages 1083-1103.
    16. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 772-802.
    17. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    18. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    19. Severin Borenstein & Stephen Holland, 2005. "On the Efficiency of Competitive Electricity Markets with Time-Invariant Retail Prices," RAND Journal of Economics, The RAND Corporation, vol. 36(3), pages 469-493, Autumn.
    20. Cabral, Luis M. B., 2000. "Introduction to Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262032864, December.
    21. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    22. Uritskaya, Olga Y. & Uritsky, Vadim M., 2015. "Predictability of price movements in deregulated electricity markets," Energy Economics, Elsevier, vol. 49(C), pages 72-81.
    23. S. Davies & P. Hall, 1999. "Fractal analysis of surface roughness by using spatial data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 3-37.
    24. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    25. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    26. Zunino, Luciano & Tabak, Benjamin M. & Serinaldi, Francesco & Zanin, Massimiliano & Pérez, Darío G. & Rosso, Osvaldo A., 2011. "Commodity predictability analysis with a permutation information theory approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 876-890.
    27. Tabak, Benjamin M. & Cajueiro, Daniel O., 2007. "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility," Energy Economics, Elsevier, vol. 29(1), pages 28-36, January.
    28. Uritskaya, Olga Y. & Serletis, Apostolos, 2008. "Quantifying multiscale inefficiency in electricity markets," Energy Economics, Elsevier, vol. 30(6), pages 3109-3117, November.
    29. Lee, Chien-Chiang & Lee, Jun-De, 2009. "Energy prices, multiple structural breaks, and efficient market hypothesis," Applied Energy, Elsevier, vol. 86(4), pages 466-479, April.
    30. Alexis Jacquemin, 1987. "The New Industrial Organization: Market Forces and Strategic Behavior," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262600145, December.
    31. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    32. Cecchetti, Stephen G & Lam, Pok-sang, 1994. "Variance-Ratio Tests: Small-Sample Properties with an Application to International Output Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 177-186, April.
    33. Gebre-Mariam, Yohannes Kebede, 2011. "Testing for unit roots, causality, cointegration, and efficiency: The case of the northwest US natural gas market," Energy, Elsevier, vol. 36(5), pages 3489-3500.
    34. Olga Y. Uritskaya & Vadim M. Uritsky, 2015. "Predictability of price movements in deregulated electricity markets," Papers 1505.08117, arXiv.org.
    35. Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.
    36. Morales, Lucía & Hanly, Jim, 2018. "European power markets–A journey towards efficiency," Energy Policy, Elsevier, vol. 116(C), pages 78-85.
    37. Herbert, John H & Kreil, Erik, 1996. "US natural gas markets : How efficient are they?," Energy Policy, Elsevier, vol. 24(1), pages 1-5, January.
    38. Wright, Jonathan H, 2000. "Alternative Variance-Ratio Tests Using Ranks and Signs," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 1-9, January.
    39. repec:clg:wpaper:2008-21 is not listed on IDEAS
    40. Apostolos Serletis & Ioannis Andreadis, 2007. "Random Fractal Structures in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 18, pages 245-255, World Scientific Publishing Co. Pte. Ltd..
    41. Martina, Esteban & Rodriguez, Eduardo & Escarela-Perez, Rafael & Alvarez-Ramirez, Jose, 2011. "Multiscale entropy analysis of crude oil price dynamics," Energy Economics, Elsevier, vol. 33(5), pages 936-947, September.
    42. Kim, Hongseok & Oh, Gabjin & Kim, Seunghwan, 2011. "Multifractal analysis of the Korean agricultural market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4286-4292.
    43. Panas, Epaminondas, 1991. "A weak form evaluation of the efficiency of the Rotterdam and Italian oil spot markets," Energy Economics, Elsevier, vol. 13(1), pages 26-32, January.
    44. Roll, Richard, 1972. "Interest Rates on Monetary Assets and Commodity Price Index Changes," Journal of Finance, American Finance Association, vol. 27(2), pages 251-277, May.
    45. Serletis, Apostolos & Rosenberg, Aryeh Adam, 2009. "Mean reversion in the US stock market," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 2007-2015.
    46. Redl, Christian & Haas, Reinhard & Huber, Claus & Böhm, Bernhard, 2009. "Price formation in electricity forward markets and the relevance of systematic forecast errors," Energy Economics, Elsevier, vol. 31(3), pages 356-364, May.
    47. Hany A. Shawky & Achla Marathe & Christopher L. Barrett, 2003. "A first look at the empirical relation between spot and futures electricity prices in the United States," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 23(10), pages 931-955, October.
    48. Danthine, Jean-Pierre, 1977. "Martingale, market efficiency and commodity prices," European Economic Review, Elsevier, vol. 10(1), pages 1-17.
    49. Wang, Yudong & Liu, Li, 2010. "Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis," Energy Economics, Elsevier, vol. 32(5), pages 987-992, September.
    50. Kim, Min Jae & Kim, Sehyun & Jo, Yong Hwan & Kim, Soo Yong, 2011. "Dependence structure of the commodity and stock markets, and relevant multi-spread strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3842-3854.
    51. Gideon Rosenbluth, 1955. "Measures of Concentration," NBER Chapters, in: Business Concentration and Price Policy, pages 57-99, National Bureau of Economic Research, Inc.
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