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Why do carbon prices and price volatility change?

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  • Ibrahim, Boulis Maher
  • Kalaitzoglou, Iordanis Angelos

Abstract

An asymmetric information microstructural pricing model is proposed in which price responses to information and liquidity vary with every transaction. bid-ask quotes and price components account for learning by incorporating changing expectations of the rate of transacted volume (trading intensity) and the risk level of incoming trades. Analysis of European carbon futures transactions finds expected trading intensity to simultaneously increase the information component and decrease the liquidity component of price changes, but at different rates. This explains some conflicting results in prior literature. Further, the expected persistence in trading intensity explains the majority of the autocorrelations in the level and the conditional variance of price change; helps predict hourly patterns in returns, variance and the bid-ask spread; and differentiates the price impact of buy versus sell and continuing versus reversing trades.

Suggested Citation

  • Ibrahim, Boulis Maher & Kalaitzoglou, Iordanis Angelos, 2016. "Why do carbon prices and price volatility change?," Journal of Banking & Finance, Elsevier, vol. 63(C), pages 76-94.
    • Handle: RePEc:eee:jbfina:v:63:y:2016:i:c:p:76-94
    DOI: 10.1016/j.jbankfin.2015.11.004
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    2. Federico Galán-Valdivieso & Elena Villar-Rubio & María-Dolores Huete-Morales, 2018. "The erratic behaviour of the EU ETS on the path towards consolidation and price stability," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 18(5), pages 689-706, October.
    3. Huong Le & Andros Gregoriou, 2020. "How Do You Capture Liquidity? A Review Of The Literature On Low‐Frequency Stock Liquidity," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 1170-1186, December.
    4. Yinpeng Zhang & Zhixin Liu & Yingying Xu, 2018. "Carbon price volatility: The case of China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-15, October.
    5. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    6. Wenjun Chu & Shanglei Chai & Xi Chen & Mo Du, 2020. "Does the Impact of Carbon Price Determinants Change with the Different Quantiles of Carbon Prices? Evidence from China ETS Pilots," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    7. Zhikai Zhang & Yaojie Zhang & Yudong Wang & Qunwei Wang, 2024. "The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 557-584, April.
    8. Rannou, Yves, 2019. "Limit order books, uninformed traders and commodity derivatives: Insights from the European carbon futures," Economic Modelling, Elsevier, vol. 81(C), pages 387-410.
    9. 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).
    10. Kalaitzoglou, Iordanis Angelos & Ibrahim, Boulis Maher, 2023. "Market conditions and order-type preference," International Review of Financial Analysis, Elsevier, vol. 87(C).
    11. 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.
    12. Man, Yuanyuan & Zhang, Sunpei & He, Yongda, 2024. "Dynamic risk spillover and hedging efficacy of China’s carbon-energy-finance markets: Economic policy uncertainty and investor sentiment non-linear causal effects," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1397-1416.
    13. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.
    14. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.

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    More about this item

    Keywords

    CO2 emission allowances; Market microstructure; Duration; Liquidity; Price discovery;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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