This study examines the dynamic volatility spillovers and portfolio implications of clean and dirty cryptocurrencies (DCs) over recent crisis periods, employing a two-stage approach. First, we model the evolving connectedness structure using a time-varying parameter vector autoregression (TVP-VAR) framework. Second, we assess portfolio performance through back testing four strategies, namely minimum variance portfolio (MVP), minimum correlation portfolio (MCP), minimum connectedness and risk parity, focusing on hedging effectiveness (HE) and dynamic Sharpe ratios (SRs). The analysis covers the period from 16 September 2022 to 27 February 2025, a time marked by heightened market volatility and uncertainty. Our findings reveal that clean cryptocurrencies (CCs) such as Binance Coin (BNB), Ethereum (ETH) and Cardano consistently enhance portfolio diversification, risk mitigation and stability compared to traditional dirty assets like Bitcoin (BTC), Monero and Dash. Robustness checks across alternative asset combinations confirm the consistency of these results. These insights highlight the strategic value of integrating clean digital assets to achieve both financial resilience and sustainable investment objectives in the evolving cryptocurrency market.
AliF., KhurramM. U., SensoyA., & VoX. V. (2024). Green cryptocurrencies and portfolio diversification in the era of greener paths. Renewable and Sustainable Energy Reviews, 191, 114137. https://doi.org/10.1016/j.rser.2023.114137
2.
AntonakakisN., ChatziantoniouI., & GabauerD. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084
3.
AroraR., KapoorM., SinghN., & YaqubM. Z. (2025). Green cryptocurrency and business strategies: Framework and insights from a stewardship literature review. Business Strategy and the Environment, 34(1), 804–829. https://doi.org/10.1002/bse.3996
4.
BenammarR., BoubakerA., & ElmelkiA. (2023). Is cryptocurrency still a safe haven for assets in light of the COVID-19 waves? Evidence from wavelet coherence analysis. International Journal of Financial Markets and Derivatives, 9(3). https://doi.org/10.1504/IJFMD.2023.133454
5.
BenammarR., ElmelkiA., ArfaouiN., & BoubakerA. (2025). The impact of geopolitical risk and uncertainty on cryptocurrency: Evidence from the Russia–Ukraine war. Journal of Public Affairs, 25(2), e70024. https://doi.org/10.1002/pa.70024
6.
BouriE., MolnárP., AzziG., RoubaudD., & HagforsL. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?Finance Research Letters, 20, 192–198. https://doi.org/10.1016/j.frl.2016.09.025
7.
ChatziantoniouI., & GabauerD. (2021). EMU risk-synchronisation and financial fragility through the prism of dynamic connectedness. The Quarterly Review of Economics and Finance, 97, 1–14. https://doi.org/10.1016/j.qref.2021.03.013
8.
ChenK., & YangJ. J. (2024). Asymmetric dynamic correlations and portfolio management between Bitcoin and stablecoins. Applied Economics, 1–25. https://doi.org/10.1080/00036846.2024.2308952
9.
ChristoffersenP., ErrunzaV., JacobsK., & JinX. (2014). Correlation dynamics and international diversification benefits. International Journal of Forecasting, 30(3), 807–824. https://doi.org/10.1016/j.ijforecast.2013.05.004
10.
ConlonT., CorbetS., & McGeeR. J. (2020). Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic. Research in International Business and Finance, 54, 101248. https://doi.org/10.1016/j.ribaf.2020.101248
11.
CoinMarketCap. (2023). Cryptocurrency prices, charts and market capitalizations. https://coinmarketcap.com
12.
Crypto Climate Accord. (2023). Make Crypto green. https://cryptoclimate.org/
13.
Di FeboE. (2021). From Bitcoin to carbon allowances: An asymmetric extreme risk spillover analysis. Journal of Environmental Economics and Management, 108, 102428. https://doi.org/10.1016/j.jeem.2021.102428
14.
Digiconomist. (2021). Bitcoin energy consumption index. https://digiconomist.net/bitcoin-energy-consumption
15.
DiasR., ChambinoM., GalvãoR., AlexandreP., & IrfanM. (2024). Side effects and interactions: Exploring the relationship between dirty and green cryptocurrencies and clean energy stock indices. International Journal of Energy Economics and Policy, 14(3), 411–416. https://doi.org/10.32479/ijeep.15873
16.
DieboldF. X., & YilmazK. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
17.
DieboldF. X., & YilmazK. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
18.
DuanK., HuangY., ZhaoY., & UrquhartA. (2023). Do clean and dirty cryptocurrencies connect with financial assets differently? The role of economic policy uncertainty. Energy Economics, 127, 107079. https://doi.org/10.1016/j.eneco.2023.107079
19.
DuanK., ZhangL., UrquhartA., YaoK., & PengL. (2024). Do clean and dirty cryptocurrencies connect financial assets differently? The perspective of market inefficiency. Research in International Business and Finance, 70(Part B), 102351. https://doi.org/10.1016/j.ribaf.2024.102351
20.
EderingtonL. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157–170. https://doi.org/10.2307/2327150
21.
EsparciaC., FakhfakhT., & JareñoF. (2024). The green, the dirty and the stable: Diversifying equity portfolios by adding tokens of different nature. The North American Journal of Economics and Finance, 69, 102020. https://doi.org/10.1016/j.najef.2023.102020
22.
GabauerD. (2021). Dynamic measures of asymmetric and pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, 100680. https://doi.org/10.1016/j.mulfin.2021.100680
23.
GaiesB., ChaâbaneN., ArfaouiN., & SahutJ.-M. (2024). On the resilience of cryptocurrencies: A quantile-frequency analysis of Bitcoin and Ethereum reactions in times of inflation and financial instability. Research in International Business and Finance, 70(Part A), 102302. https://doi.org/10.1016/j.ribaf.2024.102302
24.
HaqI. U., & BouriE. (2022). Sustainable versus conventional cryptocurrencies in the face of cryptocurrency uncertainty indices: An analysis across time and scales. Journal of Risk and Financial Management, 15(10), 442. https://doi.org/10.3390/jrfm15100442
25.
HowsonP., & de VriesA. (2022). Preying on the poor? Opportunities and challenges for tackling the social and environmental threats of cryptocurrencies for vulnerable and low-income communities. Energy Research & Social Science, 84, 102394. https://doi.org/10.1016/j.erss.2021.102394
26.
HusainA., YiiK. J., & LeeC. (2023). Are green cryptocurrencies really green? New evidence from wavelet analysis. Journal of Cleaner Production, 417, 137985. https://doi.org/10.1016/j.jclepro.2023.137985
27.
JamesN., & MenziesM. D. (2023). Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies. Entropy, 25(6), 931. https://doi.org/10.3390/e25060931
28.
JanaR. K., GhoshI., DasD., & DuttaA. (2021). Determinants of electronic waste generation in the Bitcoin network: Evidence from the machine learning approach. Technological Forecasting and Social Change, 173, 121101. https://doi.org/10.1016/j.techfore.2021.121101
29.
JiQ., ZhangD., & ZhaoY. (2020). Searching for safe-haven assets during the COVID-19 pandemic. International Review of Financial Analysis, 71, 101526. https://doi.org/10.1016/j.irfa.2020.101526
30.
KatsiampaP., YarovayaL., & ZiębaD. (2022). High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis. Journal of International Financial Markets, Institutions and Money, 79, 101578. https://doi.org/10.1016/j.intfin.2022.101578
31.
KiliçM., & AltanI. M. (2023). Are green cryptocurrencies safe? Investigation of the green and non-green cryptocurrencies. Akademik Yaklaşımlar Dergisi, 14(2), 651–663.
32.
KöhlerS., & PizzolM. (2019). Life cycle assessment of Bitcoin mining. Environmental Science and Technology, 53(24), 13598–13606. https://doi.org/10.1021/acs.est.9b05687
33.
KrauseM. J., & TolaymatT. (2018). Quantification of energy and carbon costs for mining cryptocurrencies. Nature Sustainability, 1(11), 711–718. https://doi.org/10.1038/s41893-018-0152-7
34.
KristjanpollerW., & TabakB. M. (2025). Multifractal cross-correlations of dirty and clean cryptocurrencies with main financial indices. Physica A: Statistical Mechanics and Its Applications, 668, 130541. https://doi.org/10.1016/j.physa.2025.130541
35.
KumarS. (2021). Review of geothermal energy as an alternate energy source for Bitcoin mining. Journal of Economics and Economic Education Research, 23(1), 1–12.
36.
LahmiriS., & BekirosS. (2019). Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering. Chaos, Solitons & Fractals, 127, 334–341. https://doi.org/10.1016/j.chaos.2019.07.012
37.
MaillardS., RoncalliT., & TeïletcheJ. (2010). The properties of equally weighted risk contribution portfolios. The Journal of Portfolio Management, 36(4), 60–70. https://doi.10.3905/jpm.2010.36.4.060
38.
MiśkiewiczR., MatanK., & KarnowskiJ. (2022). The role of crypto trading in the economy, renewable energy consumption, and ecological degradation. Energies, 15(10), 3805. https://doi.org/10.3390/en15103805
39.
MnifE., & JarbouiA. (2021). Islamic, green, and conventional cryptocurrency market efficiency during the COVID-19 pandemic. Journal of Islamic Monetary Economics and Finance, 7. https://doi.org/10.21098/jimf.v7i0.1315
40.
MohsinM., NaseemS., Zia-ur-RehmanM., BaigS. A., & SalamatS. (2020). The crypto-trade volume, GDP, energy use, and environmental degradation sustainability: An analysis of the top 20 crypto-trader countries. International Journal of Finance and Economics, 27(1), 651–667. https://doi.org/10.1002/ijfe.2185
41.
MoraC., RollinsR. L., TaladayK., KantarM. B., ChockM. K., ShimadaM., & FranklinE. C. (2018). Bitcoin emissions alone could push global warming above 2°C. Nature Climate Change, 8(11), 931–933. https://doi.org/10.1038/s41558-018-0321-8
42.
PatelR., KumarS., BouriE., & IqbalN. (2023). Spillovers between green and dirty cryptocurrencies and socially responsible investments around the war in Ukraine. International Review of Economics and Finance, 87, 143–162. https://doi.org/10.1016/j.iref.2023.04.013
43.
PhamL., KarimS., NaeemM. A., & LongC. (2022). A tale of two tails among carbon prices, green and non-green cryptocurrencies. International Review of Financial Analysis, 82, 102139. https://doi.org/10.1016/j.irfa.2022.102139
44.
QiaoX., ZhuH., & HauL. (2020). Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis. International Review of Financial Analysis, 71, 101541. https://doi.org/10.1016/j.irfa.2020.101541
45.
RenB., & LuceyB. (2022a). A clean, green haven?—Examining the relationship between clean energy, clean and dirty cryptocurrencies. Energy Economics, 109, 105951. https://doi.org/10.1016/j.eneco.2022.105951
46.
RenB., & LuceyB. (2022b). Do clean and dirty cryptocurrency markets herd differently?Finance Research Letters, 47, 102795. https://doi.org/10.1016/j.frl.2022.102795
47.
RenB., LuceyB., & LuoQ. (2023). An examination of green bonds as a hedge and safe haven for international equity markets. Global Finance Journal, 58, 100894. https://doi.org/10.1016/j.gfj.2023.100894
48.
SchinckusC. (2021). Proof-of-work based blockchain technology and Anthropocene: An undermined situation?Renewable and Sustainable Energy Reviews, 152, 111682. https://doi.org/10.1016/j.rser.2021.111682
49.
SchinckusC., NguyenC. P., & ChongF. H. L. (2020). Crypto-currencies trading and energy consumption. International Journal of Energy Economics and Policy, 10(3), 355–364. https://doi.org/10.32479/ijeep.9217
50.
SedlmeirJ., BuhlH. U., FridgenG., & KellerR. (2020). The energy consumption of blockchain technology: Beyond myth. Business & Information Systems Engineering, 62(6), 599–608. https://doi.org/10.1007/s12599-020-00656-x
51.
SharifA., BrahimM., DoganE., & TzeremesP. (2023). Analysis of the spillover effects between green economy, clean, and dirty cryptocurrencies. Energy Economics, 120, 106594. https://doi.org/10.1016/j.eneco.2023.106594
52.
SharpeW. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119–138. https://doi.org/10.1086/294846
53.
SinghaniaM., SethS., & SainiC. (2025). Quantile connectedness among green and dirty cryptocurrencies, and clean technology. https://doi.org/10.2139/ssrn.5095972
54.
TedeschiM., MighriZ., TiwariA. K., & SarwarS. (2023). A quantile-time-frequency connectedness investigation through the dirty and clean cryptocurrencies spillover. Journal of Cleaner Production, 425, 138889. https://doi.org/10.1016/j.jclepro.2023.138889
55.
YanC., ZhangL., & BouriE. (2024). Co-bubble transmission across clean and dirty cryptocurrencies: Network and portfolio analysis. Journal of International Money and Finance, 145, 103108. https://doi.org/10.1016/j.jimonfin.2024.103108
56.
YuM., XuK., & ZhengX. (2024). Withdrawn: Mimicking crypto portfolios in sustainable investment. The British Accounting Review, 56(6), 101463. https://doi.org/10.1016/j.bar.2024.101463
57.
ZhouE., & WangX. (2024). Who are the receivers and transmitters of volatility spillovers: Oil, clean energy, and green and non-green cryptocurrency markets. Environmental Science and Pollution Research, 31(4), 5735–5761. https://doi.org/10.1007/s11356-023-29918-3
