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Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence

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  • Sattarhoff, Cristina
  • Gronwald, Marc

Abstract

This paper introduces a new method for measuring nonlinear predictability in financial price changes: the so-called intermittency coefficient, a parameter of the multifractal random walk model by Bacry et al. (2001). As the intermittency coefficient can quantify the degree of nonlinear deviation from a random walk, we employ its estimates from financial data as a proxy for the loss of financial market efficiency. In addition, we propose a new statistical test of the random walk hypothesis. In an empirical application using data from the largest currently existing market for tradable pollution permits, the European Union Emissions Trading Scheme (EU ETS), we show that the degree of efficiency of this market remains largely unchanged over the period of observation 2008–2019. This suggests that the market has reached a mature state: informational efficiency in Phase III remains at a level comparable to Phase II. What is more, the EU ETS is found to be more efficient than the US stock market. This result, surprising as such, is largely attributable to the lower exposure to global economic shocks of the EU ETS.

Suggested Citation

  • Sattarhoff, Cristina & Gronwald, Marc, 2022. "Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence," International Review of Financial Analysis, Elsevier, vol. 84(C).
  • Handle: RePEc:eee:finana:v:84:y:2022:i:c:s1057521922003532
    DOI: 10.1016/j.irfa.2022.102403
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    as
    1. de Perthuis, Christian & Trotignon, Raphael, 2014. "Governance of CO2 markets: Lessons from the EU ETS," Energy Policy, Elsevier, vol. 75(C), pages 100-106.
    2. Bacry, E. & Delour, J. & Muzy, J.F., 2001. "Modelling financial time series using multifractal random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 84-92.
    3. Ottmar Edenhofer, 2014. "Reforming emissions trading," Nature Climate Change, Nature, vol. 4(8), pages 663-664, August.
    4. Reyer Gerlagh & Roweno J. R. K. Heijmans & Knut Einar Rosendahl, 2020. "COVID-19 Tests the Market Stability Reserve," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 855-865, August.
    5. Rannou, Yves & Barneto, Pascal, 2016. "Futures trading with information asymmetry and OTC predominance: Another look at the volume/volatility relations in the European carbon markets," Energy Economics, Elsevier, vol. 53(C), pages 159-174.
    6. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    7. David, S.A. & Inácio, C.M.C. & Quintino, D.D. & Machado, J.A.T., 2020. "Measuring the Brazilian ethanol and gasoline market efficiency using DFA-Hurst and fractal dimension," Energy Economics, Elsevier, vol. 85(C).
    8. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    9. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2012. "Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1607-1626.
    10. Lim, Kian-Ping & Brooks, Robert D., 2010. "Why Do Emerging Stock Markets Experience More Persistent Price Deviations From A Random Walk Over Time? A Country-Level Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 14(S1), pages 3-41, May.
    11. Koch, Nicolas & Fuss, Sabine & Grosjean, Godefroy & Edenhofer, Ottmar, 2014. "Causes of the EU ETS price drop: Recession, CDM, renewable policies or a bit of everything?—New evidence," Energy Policy, Elsevier, vol. 73(C), pages 676-685.
    12. repec:dau:papers:123456789/13539 is not listed on IDEAS
    13. 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.
    14. Palao, Fernando & Pardo, Angel, 2012. "Assessing price clustering in European Carbon Markets," Applied Energy, Elsevier, vol. 92(C), pages 51-56.
    15. Chevallier, Julien, 2013. "Variance risk-premia in CO2 markets," Economic Modelling, Elsevier, vol. 31(C), pages 598-605.
    16. Zhuang, Xiaoyang & Wei, Yu & Zhang, Bangzheng, 2014. "Multifractal detrended cross-correlation analysis of carbon and crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 113-125.
    17. Daskalakis, George, 2013. "On the efficiency of the European carbon market: New evidence from Phase II," Energy Policy, Elsevier, vol. 54(C), pages 369-375.
    18. repec:dau:papers:123456789/11713 is not listed on IDEAS
    19. Hintermann, Beat, 2010. "Allowance price drivers in the first phase of the EU ETS," Journal of Environmental Economics and Management, Elsevier, vol. 59(1), pages 43-56, January.
    20. Dominik M. Rösch & Avanidhar Subrahmanyam & Mathijs A. van Dijk, 2017. "The Dynamics of Market Efficiency," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1151-1187.
    21. Beat Hintermann & Sonja Peterson & Wilfried Rickels, 2016. "Price and Market Behavior in Phase II of the EU ETS: A Review of the Literature," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 10(1), pages 108-128.
    22. Fernando Palao & Angel Pardo, 2018. "Do Price Barriers Exist in the European Carbon Market?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(1), pages 111-124, January.
    23. Corgnet, Brice & DeSantis, Mark & Porter, David, 2020. "The distribution of information and the price efficiency of markets," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    24. Calvet, Laurent & Fisher, Adlai, 2001. "Forecasting multifractal volatility," Journal of Econometrics, Elsevier, vol. 105(1), pages 27-58, November.
    25. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    26. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    27. Deeney, Peter & Cummins, Mark & Dowling, Michael & Smeaton, Alan F., 2016. "Influences from the European Parliament on EU emissions prices," Energy Policy, Elsevier, vol. 88(C), pages 561-572.
    28. Friedrich, Marina & Mauer, Eva-Maria & Pahle, Michael & Tietjen, Oliver, 2020. "From fundamentals to financial assets: the evolution of understanding price formation in the EU ETS," EconStor Preprints 196150, ZBW - Leibniz Information Centre for Economics, revised 2020.
    29. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    30. Tiwari, Aviral Kumar & Kumar, Satish & Pathak, Rajesh & Roubaud, David, 2019. "Testing the oil price efficiency using various measures of long-range dependence," Energy Economics, Elsevier, vol. 84(C).
    31. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    32. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    33. 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.
    34. Bacry, E. & Kozhemyak, A. & Muzy, Jean-Francois, 2008. "Continuous cascade models for asset returns," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 156-199, January.
    35. repec:dau:papers:123456789/10174 is not listed on IDEAS
    36. Alfarano, Simone & Lux, Thomas, 2007. "A Noise Trader Model As A Generator Of Apparent Financial Power Laws And Long Memory," Macroeconomic Dynamics, Cambridge University Press, vol. 11(S1), pages 80-101, November.
    37. Batten, Jonathan A. & Maddox, Grace E. & Young, Martin R., 2021. "Does weather, or energy prices, affect carbon prices?," Energy Economics, Elsevier, vol. 96(C).
    38. Ekkehart Boehmer & Eric K. Kelley, 2009. "Institutional Investors and the Informational Efficiency of Prices," The Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3563-3594, September.
    39. Yun-Jung Lee & Neung-Woo Kim & Ki-Hong Choi & Seong-Min Yoon, 2020. "Analysis of the Informational Efficiency of the EU Carbon Emission Trading Market: Asymmetric MF-DFA Approach," Energies, MDPI, vol. 13(9), pages 1-14, May.
    40. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    41. Plott, Charles R & Sunder, Shyam, 1988. "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Econometrica, Econometric Society, vol. 56(5), pages 1085-1118, September.
    42. Lutz, Benjamin Johannes & Pigorsch, Uta & Rotfuß, Waldemar, 2013. "Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals," Energy Economics, Elsevier, vol. 40(C), pages 222-232.
    43. Duan, Kun & Li, Zeming & Urquhart, Andrew & Ye, Jinqiang, 2021. "Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach," International Review of Financial Analysis, Elsevier, vol. 75(C).
    44. Kian-Ping Lim & Chee-Wooi Hooy, 2013. "Non-Linear Predictability In G7 Stock Index Returns," Manchester School, University of Manchester, vol. 81(4), pages 620-637, July.
    45. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    46. Charles, Amélie & Darné, Olivier & Fouilloux, Jessica, 2011. "Testing the martingale difference hypothesis in CO2 emission allowances," Economic Modelling, Elsevier, vol. 28(1-2), pages 27-35, January.
    47. Palao, Fernando & Pardo, Ángel, 2014. "What makes carbon traders cluster their orders?," Energy Economics, Elsevier, vol. 43(C), pages 158-165.
    48. Wang, Yudong & Liu, Li & Gu, Rongbao, 2009. "Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 271-276, December.
    49. Ekkehart Boehmer & Juan (Julie) Wu, 2013. "Short Selling and the Price Discovery Process," The Review of Financial Studies, Society for Financial Studies, vol. 26(2), pages 287-322.
    50. Kristoufek, Ladislav, 2019. "Are the crude oil markets really becoming more efficient over time? Some new evidence," Energy Economics, Elsevier, vol. 82(C), pages 253-263.
    51. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
    52. Jorge Perez-Rodriguez & Salvador Torra & Julian Andrada-Felix, 2005. "Are Spanish Ibex35 stock future index returns forecasted with non-linear models?," Applied Financial Economics, Taylor & Francis Journals, vol. 15(14), pages 963-975.
    53. Jiménez-Rodríguez, Rebeca, 2019. "What happens to the relationship between EU allowances prices and stock market indices in Europe?," Energy Economics, Elsevier, vol. 81(C), pages 13-24.
    54. Bredin, Don & Hyde, Stuart & Muckley, Cal, 2014. "A microstructure analysis of the carbon finance market," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 222-234.
    55. Duan, Kun & Ren, Xiaohang & Shi, Yukun & Mishra, Tapas & Yan, Cheng, 2021. "The marginal impacts of energy prices on carbon price variations: Evidence from a quantile-on-quantile approach," Energy Economics, Elsevier, vol. 95(C).
    56. Ibarra-Valdez, C. & Alvarez, J. & Alvarez-Ramirez, J., 2016. "Randomness confidence bands of fractal scaling exponents for financial price returns," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 119-124.
    57. Emmanuel Bacry & Alexey Kozhemyak & J.-F. Muzy, 2008. "Continuous cascade models for asset returns," Post-Print hal-00604449, HAL.
    58. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.
    59. Perino, Grischa & Willner, Maximilian, 2016. "Procrastinating reform: The impact of the market stability reserve on the EU ETS," Journal of Environmental Economics and Management, Elsevier, vol. 80(C), pages 37-52.
    60. Jochen Heberle & Cristina Sattarhoff, 2017. "A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators," Econometrics, MDPI, vol. 5(1), pages 1-16, January.
    61. Khediri, Karim Ben & Charfeddine, Lanouar, 2015. "Evolving efficiency of spot and futures energy markets: A rolling sample approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 6(C), pages 67-79.
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    Cited by:

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    More about this item

    Keywords

    Weak-form market efficiency; Degree of market efficiency; Multifractality; Multifractal random walk; European union emissions trading scheme;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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