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The multi-scale analysis of dynamic transmission volatility of carbon prices

Author

Listed:
  • Ange Nsouadi

    (LARES, University Marien Ngouabi Of Congo)

  • Virginie Terraza

    (DEM, University of Luxembourg, Faculty of Law, Economics and Finance)

Abstract

The implementation of the EU ETS in 2005 led to the establishment of a price that enables manufacturers to realize the impact of their activities on the environment clean. There are no items in this day, since the creation of the European carbon market, which has focused on the analysis of volatility transmission between different investment horizons. The purpose of this paper is to fill this gap in the literature. we analyze the volatility of the price of carbon quota (EUA), by studying linear and nonlinear causal relationships of wavelet components between the different volatilities that we captured at different time scales. we initially conducted the decomposition of the EUA price volatility at different time-frequency interval using a wavelet approach. Our study will be to examine whether the volatility is transmitted from the high-frequency structure of the carbon price in the low frequency. Our results show an intra-structural dependance in carbon price volatility. We detect instability in the volatility of carbon and observe the existence of a bidirectional relationship from high frequency traders to low frequency traders. Our study showed that high-frequency shocks yields carbon price can have a significant impact beyond their Fontiers and touch the low frequency structure associated with long-term traders enables manufacturers to realize the impact of their activities on the environment clean. There are no items in this day, since the creation of the European carbon market, which has focused on the analysis of volatility transmission between different investment horizons. The purpose of this paper is to fill this gap in the literature. we analyze the volatility of the price of carbon quota (EUA), by studying linear and nonlinear causal relationships of wavelet components between the different volatilities that we captured at different time scales. we initially conducted the decomposition of the EUA price volatility at different time-frequency interval using a wavelet approach. Our study will be to examine whether the volatility is transmitted from the high-frequency structure of the carbon price in the low frequency. Our results show an intra-structural dependance in carbon price volatility. We detect instability in the volatility of carbon and observe the existence of a bidirectional relationship from high frequency traders to low frequency traders. Our study showed that high-frequency shocks yields carbon price can have a significant impact beyond their Fontiers and touch the low frequency structure associated with long-term traders

Suggested Citation

  • Ange Nsouadi & Virginie Terraza, 2024. "The multi-scale analysis of dynamic transmission volatility of carbon prices," Economics Bulletin, AccessEcon, vol. 44(1), pages 399-415.
    • Handle: RePEc:ebl:ecbull:eb-21-00911
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    References listed on IDEAS

    as
    1. Benhmad, François, 2012. "Modeling nonlinear Granger causality between the oil price and U.S. dollar: A wavelet based approach," Economic Modelling, Elsevier, vol. 29(4), pages 1505-1514.
    2. Naccache, Théo, 2011. "Oil price cycles and wavelets," Energy Economics, Elsevier, vol. 33(2), pages 338-352, March.
    3. Benhmad, François, 2013. "Dynamic cyclical comovements between oil prices and US GDP: A wavelet perspective," Energy Policy, Elsevier, vol. 57(C), pages 141-151.
    4. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Carbon market; EU ETS; Wavelet; time-scale; Granger Causality;
    All these keywords.

    JEL classification:

    • Y1 - Miscellaneous Categories - - Data: Tables and Charts
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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