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Predicting cryptocurrency volatility: Climate policy uncertainty versus environmental attention

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  • Slim, Skander
  • Osman, Mohamed
  • Koubaa, Yosra

Abstract

In this paper, we examine the differential effects of Climate Policy Uncertainty (CPU) and Cryptocurrency Environmental Attention (ICEA) on monthly realized volatility of cryptocurrencies. Considering the rising concerns about the environmental sustainability of this emerging asset class, we distinguish between dirty and clean cryptocurrencies based on their energy consumption levels. Results reveal that CPU exerts a significant nonlinear impact on cryptocurrency volatility, the relevance of which is supported by its superior predictive ability over alternative specifications incorporating ICEA, under statistical and economic evaluation metrics. By incorporating climate-related variables into volatility modeling, this study contributes to the growing literature on sustainable finance and offers novel insights into the interplay between environmental factors and cryptocurrency market dynamics.

Suggested Citation

  • Slim, Skander & Osman, Mohamed & Koubaa, Yosra, 2026. "Predicting cryptocurrency volatility: Climate policy uncertainty versus environmental attention," Finance Research Letters, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:finlet:v:92:y:2026:i:c:s1544612326000851
    DOI: 10.1016/j.frl.2026.109554
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    1. Ströbel, Johannes & Wurgler, Jeffrey, 2021. "What do you think about climate finance?," CEPR Discussion Papers 16622, Centre for Economic Policy Research.
    2. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    3. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    4. Raza, Syed Ali & Khan, Komal Akram & Guesmi, Khaled & Benkraiem, Ramzi, 2023. "Uncertainty in the financial regulation policy and the boom of cryptocurrencies," Finance Research Letters, Elsevier, vol. 52(C).
    5. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    6. Manel Mahjoubi & Jamel Eddine Henchiri, 2024. "The effect of policy uncertainty on the volatility of bitcoin," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 16(4), pages 429-441, May.
    7. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    8. Cepni, Oguzhan & Demirer, Riza & Rognone, Lavinia, 2022. "Hedging climate risks with green assets," Economics Letters, Elsevier, vol. 212(C).
    9. Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
    10. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    11. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    12. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    13. Pengxiang Zhai & Ying Fan & Qiang Ji & Yan-Ran Ma, 2024. "Climate Risks And Financial Markets: A Review Of The Literature," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-25, November.
    14. Yen, Kuang-Chieh & Cheng, Hui-Pei, 2021. "Economic policy uncertainty and cryptocurrency volatility," Finance Research Letters, Elsevier, vol. 38(C).
    15. Lv, Wendai & Li, Bin, 2023. "Climate policy uncertainty and stock market volatility: Evidence from different sectors," Finance Research Letters, Elsevier, vol. 51(C).
    16. Jin, Daxiang & Yu, Jize, 2023. "Predicting cryptocurrency market volatility: Novel evidence from climate policy uncertainty," Finance Research Letters, Elsevier, vol. 58(PC).
    17. Ben Omrane, Walid & Saadi, Samir & Savaser, Tanseli, 2024. "Sustainable energy practices and cryptocurrency market behavior," Energy Economics, Elsevier, vol. 139(C).
    18. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    19. Ding, Shusheng & Wu, Xiangling & Cui, Tianxiang & Goodell, John W. & Du, Anna Min, 2025. "Modeling climate policy uncertainty into cryptocurrency volatilities," International Review of Financial Analysis, Elsevier, vol. 102(C).
    20. Pástor, Ľuboš & Stambaugh, Robert F. & Taylor, Lucian A., 2022. "Dissecting green returns," Journal of Financial Economics, Elsevier, vol. 146(2), pages 403-424.
    21. Yufei Xia & Yating Fu & Ziyi Zong & Qiong Zheng, 2025. "Can climate risks affect cryptocurrency volatility? Fresh evidence from a GARCH-MIDAS-X model," Applied Economics Letters, Taylor & Francis Journals, vol. 32(6), pages 803-807, March.
    22. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    23. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    24. Yizhi Wang & Brian Lucey & Samuel Alexandre Vigne & Larisa Yarovaya, 2022. "An index of cryptocurrency environmental attention (ICEA)," China Finance Review International, Emerald Group Publishing Limited, vol. 12(3), pages 378-414, January.
    25. Yao, Xiaoyang & He, Wenjing & Li, Jianfeng & Le, Wei, 2023. "Climate policy uncertainty through production networks: Evidence from the stock market," Economics Letters, Elsevier, vol. 233(C).
    26. Aras, Serkan & Özdemir, Mehmet Ozan & Çılgın, Cihan, 2025. "Uncertainty or investor attention: Which has more impact on Bitcoin volatility?," Research in International Business and Finance, Elsevier, vol. 77(PB).
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