Unveiling dynamic network connectedness and machine-intelligent portfolio optimization in green cryptocurrency and AI-token markets

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Abstract

Green cryptocurrencies and artificial intelligence (AI)-coins have emerged as prominent digital asset classes amid growing interest in sustainable finance and technological innovation. Despite their rapid development, the empirical relationship between these two segments remains largely unexplored. To address this gap, assess dynamic risk connectedness, and identify net transmitter and net receiver spillovers between green and AI cryptocurrencies, we first use a time-varying parameter vector autoregressive framework to characterize the dynamic interdependencies across time and frequency. To further account for nonlinear dependence and improve interpretability for portfolio construction, we integrate eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) and implement an AI-driven portfolio-optimization strategy spanning green cryptocurrencies and AI tokens. Empirically, the central findings indicate that Ethereum (ETH), the dominant green cryptocurrency, persistently serves as a net transmitter of volatility to other green assets and to the five major AI-crypto markets. SHAP-based attribution further shows that the portfolio-contribution metric (Z_p), together with Ripple (XRP) and ETH, ranks among the most influential features. Overall, the proposed XGBoost-based approach captures nonlinear interactions among momentum, liquidity, and volatility, enhancing forecasting and portfolio allocation. When evaluated out-of-sample over 438 daily-rebalanced trading days (August 13, 2024–October 24, 2025), the machine learning methodology attains an annualized Sharpe ratio of approximately 2.08 at mean daily volatility near 1.04%. It also achieves the highest terminal wealth among the benchmark strategies (USD 17,812 from a USD 10,000 baseline), yielding a 78.1% cumulative return and superior risk-adjusted performance relative to competing strategies. These findings provide economically intuitive evidence of cross‑segment spillovers and yield policy-relevant implications for machine-learning-based portfolio allocation across sustainable and AI cryptocurrency markets. They also offer useful insights for regulators with respect to systemic risk surveillance, contagion monitoring, and the preservation of market integrity.

Keywords

AI cryptocurrency / dynamic volatility spillover / machine learning portfolio allocation / towards Net-Zero / time-varying parameter vector autoregressive (TVP-VAR) model

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References

[1] D. B. Olawade, O. Z. Wada, A. C. David-Olawade, O. Fapohunda, A. O. Ige, J. Ling, Artificial intelligence potential for net zero sustainability: Current evidence and prospects, Next Sustainability, 4(2024), 100041. https://doi.org/10.1016/j.nxsust.2024.100041

[2] T. Lim, Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways, Artif. Intell. Rev., 57(2024), 76. https://doi.org/10.1007/s10462-024-10708-3

[3] R. Patel, S. Kumar, E. Bouri, N. Iqbal, Spillovers between green and dirty cryptocurrencies and socially responsible investments around the war in Ukraine, Int. Rev. Econ. Finance, 87(2023), 143-162. https://doi.org/10.1016/j.iref.2023.04.013

[4] M. Zeng, W. Zhang, Green finance: The catalyst for artificial intelligence and energy efficiency in Chinese urban sustainable development, Energy Econ., 139(2024), 107883. https://doi.org/10.1016/j.eneco.2024.107883

[5] L. Pham, T. L. D. Huynh, W. Hanif, Time-varying asymmetric spillovers among cryptocurrency, green and fossil-fuel investments, Glob. Finance J., 58(2023), 100891. https://doi.org/10.1016/j.gfj.2023.100891

[6] B. Bedowska-Sojka, A. Kliber, Do investors in dirty and clean cryptocurrencies care about energy efficiency in the same way? Finance Res. Lett., 67(2024), 105852. https://doi.org/10.1016/j.frl.2024.105852

[7] A. Kliber, B. Bedowska-Sojka, Proof-of-work versus proof-of-stake coins as possible hedges against green and dirty energy, Energy Econ., 138(2024), 107820. https://doi.org/10.1016/j.eneco.2023.107820

[8] A. Yakovenko, Solana: A new architecture for a high performance blockchain v0.8.13, White paper, (2018). Available from: http://tinyurl.com/hmks9mx7.

[9] S. Alizadeh, M. Khabbazian, Solana’s transaction network: Analysis, insights, and comparison, EPJ Data Sci., 14(2025), 1-16.

[10] T. L. D. Huynh, E. Hille, M. A. Nasir, Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies, Technol. Forecast. Soc. Change, 159(2020), 120188. https://doi.org/10.1016/j.techfore.2020.120188

[11] S. Demiralay, H. G. Gencer, S. Bayraci, How do artificial intelligence and robotics stocks co-move with traditional and alternative assets in the age of the 4th industrial revolution? Implications and insights for the COVID-19 period, Technol. Forecast. Soc. Change, 171(2021), 120989. https://doi.org/10.1016/j.techfore.2021.120989

[12] F. Jareno, I. Yousaf, Artificial intelligence-based tokens: Fresh evidence of connectedness with artificial intelligence-based equities, Int. Rev. Financ. Anal., 89(2023), 102826. https://doi.org/10.1016/j.irfa.2023.102826

[13] M. Abdullah, P. K. Sarker, E. J. A. Abakah, A. K. Tiwari, M. Z. Rehman, Tail risk intersection between tech-tokens and tech-stocks, Glob. Financ. J., 61(2024), 100989. https://doi.org/10.1016/j.gfj.2023.100989

[14] A. Husain, K. J. Yii, C. C. Lee, Are green cryptocurrencies really green? New evidence from wavelet analysis, J. Clean. Prod., 417(2023), 137985. https://doi.org/10.1016/j.jclepro.2023.137985

[15] K. Duan, Y. Zhao, A. Urquhart, Y. Huang, Do clean and dirty cryptocurrencies connect with financial assets differently? The role of economic policy uncertainty, Energy Econ., 127(2023), 107079. https://doi.org/10.1016/j.eneco.2023.107079

[16] M. A. Naeem, T. T. H. Nguyen, S. Karim, B. M. Lucey, Extreme downside risk transmission between green cryptocurrencies and energy markets: The diversification benefits, Finance Res. Lett., 58(2023), 104263. https://doi.org/10.1016/j.frl.2022.104263

[17] F. Ali, M. U. Khurram, A. Sensoy, X. V. Vo, Green cryptocurrencies and portfolio diversification in the era of greener paths, Renewable Sustainable Energy Rev., 191(2024), 114137. https://doi.org/10.1016/j.rser.2023.114137

[18] J. Almeida, T. C. Gonçalves, The AI revolution: Are crypto markets more efficient after ChatGPT 3? Finance Res. Lett., 66(2024), 105608. https://doi.org/10.1016/j.frl.2024.105608

[19] A. Saggu, L. Ante, The influence of ChatGPT on artificial intelligence related crypto assets: Evidence from a synthetic control analysis, Finance Res. Lett., 55(2023), 103993. https://doi.org/10.1016/j.frl.2023.103993

[20] S. Chipolina, Bitcoin leaves the rest of the crypto market behind, FT Cryptofinance Newsletter, (2023). Available from: https://www.ft.com/content/5160b007-5e68-423d-8a6c-a0fdef6199f4?syn-25a6b1a6=1.

[21] L. Ante, E. Demir, The ChatGPT effect on AI-themed cryptocurrencies, Econ. Bus. Lett., 13(2024), 29-38. https://doi.org/10.17811/ebl.14.1.2024.29-38

[22] C. Stoll, L. Klaassen, U. Gallersdorfer, The carbon footprint of Bitcoin, Joule, 3(2019), 1647-1661. https://doi.org/10.1016/j.joule.2019.05.012

[23] B. Ren, B. Lucey, A clean, green haven?-Examining the relationship between clean energy, clean and dirty cryptocurrencies, Energy Econ., 109(2022), 105951. https://doi.org/10.1016/j.eneco.2022.105951

[24] L. Pham, S. Karim, M. A. Naeem, C. Long, A tale of two tails among carbon prices, green and non-green cryptocurrencies, Int. Rev. Financ. Anal., 82(2022), 102139. https://doi.org/10.1016/j.irfa.2022.102139

[25] K. S. Chen, W. C. Ong, Dynamic correlations between Bitcoin, carbon emission, oil and gold markets: New implications for portfolio management, AIMS Math., 9(2024), 1403-1433. https://doi.org/10.3934/math.2024069

[26] S. Long, H. Tian, Z. Li, Dynamic spillovers between uncertainties and green bond markets in the US, Europe, and China: Evidence from the quantile VAR framework, Int. Rev. Financ. Anal., 84(2022), 102416. https://doi.org/10.1016/j.irfa.2022.102416

[27] K.C. Lu, K.S. Chen, Uncovering information linkages between Bitcoin, sustainable finance and the impact of COVID-19: Fractal and entropy analysis, Fractal Fract., 7(2023), 424. https://doi.org/10.3390/fractalfract7060424

[28] J. Wu, J. P. Li, C. W. Su, Can green bond hedges climate policy uncertainty in the United States? Econ. Anal. Policy , 82(2024), 1158-1176. https://doi.org/10.1016/j.eap.2023.08.015

[29] I. Yousaf, O. M. Ohikhuare, Y. Li, Y. Li, Interconnectedness between electricity and artificial intelligence-based markets during the crisis periods: Evidence from the TVP-VAR approach, Energy Econ., 139(2024), 107885. https://doi.org/10.1016/j.eneco.2024.107885

[30] S. B. Jabeur, G. Gozgor, H. Rezgui, K. S. Mohammed, Dynamic dependence between quantum computing stocks and Bitcoin: Portfolio strategies for a new era of asset classes, Int. Rev. Financ. Anal., 95(2024), 103478. https://doi.org/10.1016/j.irfa.2023.103478

[31] C. C. Wu, W. P. Chen, What’s an AI name worth? The impact of AI ETFs on their underlying stocks, Finance Res. Lett., 46(2022), 102474. https://doi.org/10.1016/j.frl.2021.102474

[32] A. K. Tiwari, E. J. A. Abakah, T. L. Le, D. I. Leyva-de la Hiz, Markov-switching dependence between artificial intelligence and carbon price: The role of policy uncertainty in the era of the 4th industrial revolution and the effect of COVID-19 pandemic, Technol. Forecast. Soc. Change, 163(2021), 120434. https://doi.org/10.1016/j.techfore.2020.120434

[33] S. Sharma, A. K. Tiwari, S. Nasreen, Are FinTech, robotics, and blockchain index funds providing diversification opportunities with emerging markets? Lessons from pre and post outbreak of COVID-19, Electron. Commer. Res., 24(2024), 341-370. https://doi.org/10.1007/s10660-022-09611-2

[34] E. Jacquier, A. Kane, A. J. Marcus, Geometric or Arithmetic Mean: A Reconsideration, Financ. Anal. J., 59(2003), 46-53. https://doi.org/10.2469/faj.v59.n6.2574

[35] A. Moreira, T. Muir, Volatility-managed portfolios, J. Finance, 72(2017), 1611-1644. https://doi.org/10.1111/jofi.12513

[36] C. R. Harvey, E. Hoyle, R. Korgaonkar, S. Rattray, M. Sargaison, O. Van Hemert, The impact of volatility targeting, SSRN Electron. J., (2018), 3175538.

[37] K. Daniel, T. J. Moskowitz, Momentum crashes, J. Financ. Econ., 122(2016), 221-247. https://doi.org/10.1016/j.jfineco.2015.12.002

[38] S. Gu, B. Kelly, D. Xiu, Empirical asset pricing via machine learning, Rev. Financ. Stud., 33(2020), 2223-2273. https://doi.org/10.1093/rfs/hhaa009

[39] L. Chen, M. Pelger, J. Zhu, Deep learning in asset pricing, Manage. Sci., 70(2024), 714-750. https://doi.org/10.1287/mnsc.2023.4695

[40] J. Jang, N. Seong, Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory, Expert Syst. Appl., 218(2023), 119556. https://doi.org/10.1016/j.eswa.2023.119556

[41] F. X. Diebold, K. Yilmaz, Better to give than to receive: Predictive directional measurement of volatility spillovers, Int. J. Forecast., 28(2012), 57-66. https://doi.org/10.1016/j.ijforecast.2011.02.006

[42] H. H. Pesaran, Y. Shin, Generalized impulse response analysis in linear multivariate models, Econ. Lett., 58(1998), 17-29. https://doi.org/10.1016/S0165-1765(97)00214-0

[43] N. Antonakakis, I. Chatziantoniou, D. Gabauer, Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions, J. Risk Financ. Manage., 13(2020), 84. https://doi.org/10.3390/jrfm13040084

[44] Q. Ji, E. Bouri, C. K. M. Lau, D. Roubaud, Dynamic connectedness and integration in cryptocurrency markets, Int. Rev. Financ. Anal., 63(2019), 257-272. https://doi.org/10.1016/j.irfa.2018.12.002

[45] I. O. Fasanya, O. Oyewole, T. Odudu, Returns and volatility spillovers among cryptocurrency portfolios, Int. J. Manag. Financ., 17(2021), 327-341. https://doi.org/10.1108/IJMF-02-2019-0074

[46] G. Koop, M. H. Pesaran, S. M. Potter, Impulse response analysis in nonlinear multivariate models, J. Econom., 74(1996), 119-147. https://doi.org/10.1016/0304-4076(95)01753-4

[47] K. Phoon, F. Koh, Robo-Advisors and wealth management, J. Altern. Invest., 20(2017), 79-94. https://doi.org/10.3905/jai.2018.20.3.079

[48] T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016), 785-794. https://doi.org/10.1145/2939672.2939785

[49] S. M. Lundberg, S. Lee, A unified approach to interpreting model predictions, in Advances in Neural Information Processing Systems, 30(2017), 4765-4774.

[50] S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, et al., From local explanations to global understanding with explainable AI for trees, Nat. Mach. Intell., 2(2020), 56-67. https://doi.org/10.1038/s42256-019-0138-9

[51] R. Fry-McKibbin, C. Y. L. Hsiao, Extremal dependence tests for contagion, Econ. Rev., 37(2018), 626-649. https://doi.org/10.1080/07474938.2018.1452356

[52] K. J. Forbes, R. Rigobon, No contagion, only interdependence: Measuring stock market co-movements, J. Finance, 57(2002), 2223-2261. https://doi.org/10.1111/0022-1082.00494

[53] R. Fry, V. L. Martin, C. Tang, A new class of tests of contagion with applications, J. Bus. Econ. Stat., 28(2010), 423-437. https://doi.org/10.1198/jbes.2009.08098

[54] P. K. Narayan, Understanding exchange rate shocks during COVID-19, Finance Res. Lett., 45(2022), 102181. https://doi.org/10.1016/j.frl.2021.102181

[55] S. Khademalomoom, P. K. Narayan, Intraday effects of the currency market, J. Int. Financ. Mark. Inst. Money, 58(2019), 65-77. https://doi.org/10.1016/j.intfin.2018.09.008

[56] J. Barunik, T. Krehlik, Measuring the frequency dynamics of financial connectedness and systemic risk, J. Financ. Econom., 16(2018), 271-296. https://doi.org/10.1093/jjfinec/nby001

[57] H. Markowitz, Portfolio selection, J. Finance, 7(1952), 77-91. https://doi.org/10.2307/2975974

[58] H. T. Wu, M. L. Yueh, Cryptocurrency risk management using Levy processes and time-varying volatility, Rev. Quant. Financ. Account., 66(2026), 33-61. https://doi.org/10.1007/s11156-025-01393-6

[59] A. Phiri, I. Anyikwa, A multiscale analysis of returns and volatility spillovers in cryptocurrency markets: A post-COVID perspective, Invest. Anal. J., 54(2025), 21-41. https://hdl.handle.net/10520/ejc-invest_v54_n1_a21

[60] T. J. Moskowitz, Y. H. Ooi, L. H. Pedersen, Time series momentum, J. Financ. Econ., 104(2012), 228-250. https://doi.org/10.1016/j.jfineco.2011.11.003