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Carbon Futures Trading and Short-Term Price Prediction: An Analysis Using the Fractal Market Hypothesis and Evolutionary Computing

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  • Marc Lamphiere

    (Dublin Energy Laboratory, Technological University Dublin, D07 EWV4 Dublin, Ireland
    School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland
    Mace Group, 8 Dublin, Ireland
    These authors contributed equally to this work.)

  • Jonathan Blackledge

    (Dublin Energy Laboratory, Technological University Dublin, D07 EWV4 Dublin, Ireland
    School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland
    Centre for Advanced Studies, Warsaw University of Technology, Plac Politechniki 1, 00-661 Warsaw, Poland
    Department of Computer Science, University of Western Cape, Robert Sobukwe Rd, Bellville, Cape Town 7535, South Africa)

  • Derek Kearney

    (Dublin Energy Laboratory, Technological University Dublin, D07 EWV4 Dublin, Ireland
    School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland
    These authors contributed equally to this work.)

Abstract

This paper presents trend prediction results based on backtesting of the European Union Emissions Trading Scheme futures market. This is based on the Intercontinental Exchange from 2005 to 2019. An alternative trend prediction strategy is taken that is predicated on an application of the Fractal Market Hypothesis (FMH) in order to develop an indicator that is predictive of short term future behaviour. To achieve this, we consider that a change in the polarity of the Lyapunov-to-Volatility Ratio precedes an associated change in the trend of the European Union Allowances (EUAs) price signal. The application of the FMH in this case is demonstrated to provide a useful tool in order to assess the likelihood of the market becoming bear or bull dominant, thereby helping to inform carbon trading investment decisions. Under specific conditions, Evolutionary Computing methods are utilised in order to optimise specific trading execution points within a trend and improve the potential profitability of trading returns. Although the approach may well be of value for general energy commodity futures trading (and indeed the wider financial and commodity derivative markets), this paper presents the application of an investment indicator for EUA carbon futures risk modelling and investment trend analysis only.

Suggested Citation

  • Marc Lamphiere & Jonathan Blackledge & Derek Kearney, 2021. "Carbon Futures Trading and Short-Term Price Prediction: An Analysis Using the Fractal Market Hypothesis and Evolutionary Computing," Mathematics, MDPI, vol. 9(9), pages 1-32, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:1005-:d:545876
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    References listed on IDEAS

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    2. Hyeonho Kim & Yujin Kim & Yongho Ko & Seungwoo Han, 2022. "Performance Comparison of Predictive Methodologies for Carbon Emission Credit Price in the Korea Emission Trading System," Sustainability, MDPI, vol. 14(13), pages 1-20, July.

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