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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, Open Access Journal, 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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    Cited by:

    1. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, Open Access Journal, vol. 14(2), pages 1-17, January.
    2. George P. Papaioannou & Christos Dikaiakos & Christos Kaskouras & George Evangelidis & Fotios Georgakis, 2020. "Granger Causality Network Methods for Analyzing Cross-Border Electricity Trading between Greece, Italy, and Bulgaria," Energies, MDPI, Open Access Journal, vol. 13(4), pages 1-26, February.
    3. Bernardina Algieri & Matthias Kalkuhl, 2019. "Efficiency and Forecast Performance of Commodity Futures Markets," American Journal of Economics and Business Administration, Science Publications, vol. 11(1), pages 19-34, June.

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    More about this item

    Keywords

    electricity market; efficiency; EMH; complexity; Hurst exponent; entropy; composite index;
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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