Research article

How do the uncertainties affect the connectedness between the green bond market and conventional financial markets? Evidence from the Russian-Ukrainian war

  • Published: 24 October 2025
  • JEL Codes: G15, G01, G11

  • In this study, we address the dynamics of connectedness between the green bond market and conventional financial markets during crisis periods and uncertainty factors influencing it. Recently, most researchers have focused on the impact of uncertainties on green bond, green finance, and conventional bond markets, and only a few of them on the impact of uncertainties on the connectedness of financial markets. Moreover, this phenomenon is underexplored during crisis periods. In response to this, we assessed the change in the connectedness between green bond and conventional financial markets, as well as the impact of uncertainties on connectedness during the Russian-Ukrainian war. The Diebold and Yilmaz's (2012, 2014) spillover index was used to assess the connectedness between green bond and conventional financial markets. Dynamic model averaging (DMA), proposed by Koop and Korobilis (2012), was applied to assess the impact of uncertainties on the connectedness. The study revealed that the total spillover index increased significantly during the Russian-Ukrainian war. Moreover, the green bond market was a receiver of shocks during the pre-war period, but a transmitter during the war period. This may indicate that it has become a less predictable and more sensitive market. Total spillover was positively affected by Economic Policy Uncertainty (EPU) and Twitter-Based Economic Uncertainty (TEU) indices, and negatively by Geopolitical Risk (GPR) and Volatility (VIX) indices. Uncertainties increased with the start of the war. The GPR index peaked the most. All uncertainty indices had a greater impact on total spillover during the war. The greater impact can be explained by increased market sensitivity, rising levels of financial stress, shifts in investor behavior, and the accelerated dissemination of information during times of crisis. The findings provide implications for portfolio managers in terms of risk management and portfolio decisions at a global level.

    Citation: Vilija Aleknevičienė, Algirdas Justinas Staugaitis, Rugilė Gudaitienė, Asta Bendoraitytė. How do the uncertainties affect the connectedness between the green bond market and conventional financial markets? Evidence from the Russian-Ukrainian war[J]. Green Finance, 2025, 7(4): 634-660. doi: 10.3934/GF.2025024

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  • In this study, we address the dynamics of connectedness between the green bond market and conventional financial markets during crisis periods and uncertainty factors influencing it. Recently, most researchers have focused on the impact of uncertainties on green bond, green finance, and conventional bond markets, and only a few of them on the impact of uncertainties on the connectedness of financial markets. Moreover, this phenomenon is underexplored during crisis periods. In response to this, we assessed the change in the connectedness between green bond and conventional financial markets, as well as the impact of uncertainties on connectedness during the Russian-Ukrainian war. The Diebold and Yilmaz's (2012, 2014) spillover index was used to assess the connectedness between green bond and conventional financial markets. Dynamic model averaging (DMA), proposed by Koop and Korobilis (2012), was applied to assess the impact of uncertainties on the connectedness. The study revealed that the total spillover index increased significantly during the Russian-Ukrainian war. Moreover, the green bond market was a receiver of shocks during the pre-war period, but a transmitter during the war period. This may indicate that it has become a less predictable and more sensitive market. Total spillover was positively affected by Economic Policy Uncertainty (EPU) and Twitter-Based Economic Uncertainty (TEU) indices, and negatively by Geopolitical Risk (GPR) and Volatility (VIX) indices. Uncertainties increased with the start of the war. The GPR index peaked the most. All uncertainty indices had a greater impact on total spillover during the war. The greater impact can be explained by increased market sensitivity, rising levels of financial stress, shifts in investor behavior, and the accelerated dissemination of information during times of crisis. The findings provide implications for portfolio managers in terms of risk management and portfolio decisions at a global level.



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    [1] Arif M, Hasan M, Alawi SM, et al. (2021) COVID-19 and time-frequency connectedness between green and conventional financial markets. Global Financ J 49: 100650. https//doi:10.1016/j.gfj.2021.100650 doi: 10.1016/j.gfj.2021.100650
    [2] Benlagha N, Hemrit W (2022) Does economic policy uncertainty matter to explain connectedness within the international sovereign bond yields? J Econ Fin 46: 1–21. https://doi.org/10.1007/s12197-021-09554-8 doi: 10.1007/s12197-021-09554-8
    [3] Caporin M, Bonaccolto G, Shahzad SJH (2023) (Quantile) Spillover Indexes: simulation-based evidence, confidence intervals and a decomposition. Available at SSRN 4629224. https//doi:10.2139/ssrn.4629224
    [4] Catania L, Nonejad N (2018) Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package. J Stat Software 84: 1–39. https//doi:10.18637/jss.v084.i11 doi: 10.18637/jss.v084.i11
    [5] Chen Y, Shi G, Hou G (2024) Time‐Frequency Connectedness between Green Bonds and Conventional Financial Markets: Evidence from China. Discr Dynam Nature Soc 2024: 6655845. https//doi:10.1155/2024/6655845 doi: 10.1155/2024/6655845
    [6] Derindere Köseoğlu S, Mercangöz B A, Khan K, et al. (2024) The impact of the Russian-Ukraine war on the stock market: A causal analysis. Applied Econom 56: 2509–2519. https//doi:10.1080/00036846.2023.2188168 doi: 10.1080/00036846.2023.2188168
    [7] Diebold FX, Yilmaz K (2012) Better to give than to receive: Predictive directional measurement of volatility spillovers. Int J Forecast 28: 57–66. https//doi:10.1016/j.ijforecast.2011.02.006 doi: 10.1016/j.ijforecast.2011.02.006
    [8] Diebold FX, Yılmaz K (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms. J Econometrics 182: 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012 doi: 10.1016/j.jeconom.2014.04.012
    [9] Drachal K (2018) Determining time-varying drivers of spot oil price in a dynamic model averaging framework. Energies 11: 1207. https//doi:10.3390/en11051207 doi: 10.3390/en11051207
    [10] Elsayed AH, Naifar N, Nasreen S, et al. (2022) Dependence structure and dynamic connectedness between green bonds and financial markets: Fresh insights from time-frequency analysis before and during COVID-19 pandemic. Energy Econ 107: 105842. https//doi:10.1016/j.eneco.2022.105842 doi: 10.1016/j.eneco.2022.105842
    [11] Ferrer R, Shahzad SJH, Soriano P (2021) Are green bonds a different asset class? Evidence from time-frequency connectedness analysis. J Cleaner Production 292: 125988. https//doi:10.1016/j.jclepro.2021.125988 doi: 10.1016/j.jclepro.2021.125988
    [12] Gyamerah SA, Asare C (2024) A critical review of the impact of uncertainties on green bonds. Green Finance 6: 78. https//doi:10.3934/GF.2024004 doi: 10.3934/GF.2024004
    [13] Jiang W, Chen Y (2024) Impact of Russia-Ukraine conflict on the time-frequency and quantile connectedness between energy, metal and agricultural markets. Resources Policy 88: 104376. https//doi:10.1016/j.resourpol.2023.104376 doi: 10.1016/j.resourpol.2023.104376
    [14] Koop G, Korobilis D (2012) Forecasting inflation using dynamic model averaging. Int Econ Rev 53: 867–886. https//doi:10.1111/j.1468-2354.2012.00704.x doi: 10.1111/j.1468-2354.2012.00704.x
    [15] Koop G, Pesaran MH, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. J Econometrics 74: 119–147. https//doi:10.1016/0304-4076(95)01753-4 doi: 10.1016/0304-4076(95)01753-4
    [16] Lu X, Huang N, Mo J, et al. (2023) Dynamics of the return and volatility connectedness among green finance markets during the COVID-19 pandemic. Energy Econ 125: 106860. https//doi:10.1016/j.eneco.2023.106860 doi: 10.1016/j.eneco.2023.106860
    [17] Martiradonna M, Romagnoli S, Santini A (2023) The beneficial role of green bonds as a new strategic asset class: Dynamic dependencies, allocation and diversification before and during the pandemic era. Energy Econ 120: 106587. https//doi:10.1016/j.eneco.2023.106587 doi: 10.1016/j.eneco.2023.106587
    [18] Mensi W, Gubareva M, Adekoya OB, et al. (2024) Quantile connectedness and network among Green bonds, Renewable Energy, and G7 sustainability markets. Renewable Energy 231: 120943. https//doi:10.1016/j.resourpol.2024.104888 doi: 10.1016/j.resourpol.2024.104888
    [19] Mensi W, Vo XV, Ko HU, et al. (2023) Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis. Econ Analysis Policy 77: 558–580. https//doi:10.1016/j.eap.2022.12.010 doi: 10.1016/j.eap.2022.12.010
    [20] Mokni K, Hammoudeh S, Ajmi AN, et al. (2020) Does economic policy uncertainty drive the dynamic connectedness between oil price shocks and gold price? Resources Policy 69: 101819. https://doi.org/10.1016/j.resourpol.2020.101819 doi: 10.1016/j.resourpol.2020.101819
    [21] Naeem MA, Adekoya OB, Oliyide JA (2021) Asymmetric spillovers between green bonds and commodities. J Cleaner Production 314: 128100. https://doi.org/10.1016/j.jclepro.2021.128100 doi: 10.1016/j.jclepro.2021.128100
    [22] Ngoepe LK, Bonga-Bonga L (2024) The connectedness of financial risk and green financial instruments: a dynamic and frequency analysis (No. 121091). University Library of Munich, Germany. Available from: https://mpra.ub.uni-muenchen.de/121091/
    [23] Oliyide JA, Adekoya OB, Marie M, et al. (2023) Green finance and commodities: Cross-market connectedness during different COVID-19 episodes. Resources Policy 85: 103916. https//doi:10.1016/j.resourpol.2023.103916 doi: 10.1016/j.resourpol.2023.103916
    [24] Pham L (2016) Is it risky to go green? A volatility analysis of the green bond market. J Sustain Financ Invest 6: 263–291. https//doi:10.1080/20430795.2016.1237244 doi: 10.1080/20430795.2016.1237244
    [25] Pesaran HH, Shin Y (1998) Generalized impulse response analysis in linear multivariate models. Econ lett 58: 17–29. https//doi:10.1016/S0165-1765(97)00214-0 doi: 10.1016/S0165-1765(97)00214-0
    [26] Polat O, Başar BD, Ekşi İH (2024) Dynamic Interlinkages between the Twitter Uncertainty Index and the Green Bond Market: Evidence from the Covid-19 Pandemic and the Russian-Ukrainian Conflict. Computat Econ 65: 2873–2889. https//doi:10.1007/s10614-024-10666-6 doi: 10.1007/s10614-024-10666-6
    [27] Reboredo JC, Ugolini A, Aiube FAL (2020) Network connectedness of green bonds and asset classes. Energy Econ 86: 104629. https//doi:10.1016/j.eneco.2019.104629 doi: 10.1016/j.eneco.2019.104629
    [28] Reboredo JC, Ugolini A (2020) Price connectedness between green bond and financial markets. Econ Model 88: 25–38. https//doi:10.1016/j.econmod.2019.09.004 doi: 10.1016/j.econmod.2019.09.004
    [29] Saeed T, Bouri E, Alsulami H (2021) Extreme return connectedness and its determinants between clean/green and dirty energy investments. Energy Econ 96: 105017. https//doi:10.1016/j.eneco.2020.105017 doi: 10.1016/j.eneco.2020.105017
    [30] Sheenan L (2023) Green bonds, conventional bonds and geopolitical risk. Fin Res Lett 58: 104587. https//doi:10.1016/j.frl.2023.104587 doi: 10.1016/j.frl.2023.104587
    [31] Sohag K, Hammoudeh S, Elsayed AH, et al. (2022) Do geopolitical events transmit opportunity or threat to green markets? Decomposed measures of geopolitical risks. Energy Econ 111: 106068. https//doi:10.1016/j.eneco.2022.106068 doi: 10.1016/j.eneco.2022.106068
    [32] Tang Y, Chen XH, Sarker PK, et al. (2023) Asymmetric effects of geopolitical risks and uncertainties on green bond markets. Techn Forecast Soc Change 189: 122348. https//doi:10.1016/j.techfore.2023.122348 doi: 10.1016/j.techfore.2023.122348
    [33] Wan Y, Wang W, He S, et al. (2023) How do uncertainties affect the connectedness of global financial markets? Changes during the Russia-Ukraine conflict. Asia-Pacific J Account Econ 31: 848–875. https//doi:10.1080/16081625.2023.2268099 doi: 10.1080/16081625.2023.2268099
    [34] Wang J, Mishra S, Sharif A, et al. (2024) Dynamic spillover connectedness among green finance and policy uncertainty: Evidence from QVAR network approach. Energy Econ 131: 107330. https//doi:10.1016/j.eneco.2024.107330 doi: 10.1016/j.eneco.2024.107330
    [35] Wei Y, Shi C, Zhou C, et al. (2024) Market volatilities vs oil shocks: Which dominate the relative performance of green bonds? Energy Econ 136: 107709. https//doi:10.1016/j.eneco.2024.107709 doi: 10.1016/j.eneco.2024.107709
    [36] Xia T, Yao C X, Geng J B (2020) Dynamic and frequency-domain spillover among economic policy uncertainty, stock and housing markets in China. Int Review Fin Analy 67: 101427. https//doi:10.1016/j.irfa.2019.101427 doi: 10.1016/j.irfa.2019.101427
    [37] Xu D, Corbet S, Hu Y, et al. (2023) Examining Dynamic Connectedness between Green Bonds and Traditional Assets During Crises. Available at SSRN 4474571. https://dx.doi.org/10.2139/ssrn.4474571
    [38] Xu D, Hu Y, Corbet S, et al. (2024) Green bonds and traditional and emerging investments: Understanding connectedness during crises. North Am J Econ Financ 72: 102142. https//doi:10.1016/j.najef.2024.102142 doi: 10.1016/j.najef.2024.102142
    [39] Yousaf I, Hunjra AI, Alshater MM, et al. (2023) Multidimensional connectedness among the volatility of global financial markets around the Russian-Ukrainian conflict. Pacific-Basin Financ J 82: 102163. https//doi:10.1016/j.pacfin.2023.102163 doi: 10.1016/j.pacfin.2023.102163
    [40] Yousaf I, Mensi W, Vo XV, et al. (2024) Dynamic spillovers and connectedness between crude oil and green bond markets. Resources Policy 89: 104594. https://doi.org/10.1016/j.resourpol.2023.104594 doi: 10.1016/j.resourpol.2023.104594
    [41] Zhang D, Chen XH, Lau CKM, et al. (2023) The causal relationship between green finance and geopolitical risk: Implications for environmental management. J Environ Manage 327: 116949. https//doi:10.1016/j.jenvman.2022.116949 doi: 10.1016/j.jenvman.2022.116949
    [42] Zhao M, Park H (2024) Quantile time-frequency spillovers among green bonds, cryptocurrencies, and conventional financial markets. Int Rev Fin Anal 93: 103198. https//doi:10.1016/j.irfa.2024.103198 doi: 10.1016/j.irfa.2024.103198
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