Using the DCC-GARCH t-Copula model, we constructed the optimal hedge ratios and the portfolio weights to examine the implications for risk management and portfolio diversification. In developed markets, the most efficient hedge combinations were developed FinTech/ developed MSCI, developed FinTech/developed ESG, developed ESG/developed MSCI, developed ESG/developed FinTech, developed MSCI/developed ESG, and emerging MSCI/ emerging ESG. Though they are costly, they led to a decrease in volatility by at least 96%. This is in line with El Khoury et al. (2022) and Nakai et al. (2016), who proved that ESG funds outperform conventional ones during crisis periods, and with Apriliyanti and Alon (2017), who stated that the social and cultural contexts appear crucial to the country-level process of technological accumulation and knowledge propagation.

7 | CONCLUSION

In the present paper, we investigated the intensity and direction of spillover effects before and during the Russian war in developed, emerging, and combined regional contexts. The data were collected from Thomson Reuters and ranged from May 8, 2020, to May 11, 2022 (the time of writing this paper). We relied on the following indices: FinTech developed index, FinTech emerging index, renewable energy developed index, renewable energy emerging index, FTSE developed ESG index, and FTSE emerging ESG index, in addition to gold developed index, gold emerging index, MSCI developed index, and MSCI emerging index.

We applied the TVP-VAR methodology to extend the dynamic connectedness of the returns of the five indices, and the DCC-GARCH t-Copula model to estimate the conditional (co) variances and DCCs and construct the optimal hedging and portfolio strategies. The TVP-VAR was employed separately in the following three contexts: developed, emerging, and combined countries and for the prewar period and during the war.

The results indicated heterogenous spillover. In developed countries, FinTech, ESG, and MSCI are net transmitters, whereas gold and renewable energy are net receivers in the prewar period and during the war. In emerging countries, ESG and MSCI are net transmitters, while FinTech, renewable energy, and gold became net receivers in both periods. When we combined the 10 indicators to obtain more global representation, we found that the war has a huge impact on the role played by some assets, mainly in emerging markets. During the war, the developed FinTech, renewable energy, gold, ESG, MSCI, and emerging renewable energy became net receivers, while the emerging FinTech, gold, ESG, and MSCI became net transmitters.

We then constructed the optimal hedge ratios and the portfolio weights to examine the implications for risk management and portfolio diversification. These weight strategies were estimated based on the conditional covariances between assets, using the DCC-GARCH t-Copula model.

We concluded that hedging the volatilities of FinTech in developed markets by taking short positions in ESG and MSCI is relatively high (USD 1.02 and USD 0.98), but effective. Similarly, the volatilities of ESG and MSCI in developed markets can be effectively hedged, reaching 99%. ESG in developed markets provides a good hedging opportunity for FinTech and MSCI in developed markets.

When a portfolio can be used by combining indices between developed and emerging markets, we can see that renewable energy in developed markets can be hedged using FinTech emerging, but is less efficient than FinTech developed, being 45% efficient rather than 86%. However, including MSCI emerging or ESG emerging with renewable energy developed is 84%
and 83% effective, indicating the important role of MSCI and ESG in both countries to hedge
renewable energy. Similarly, ESG and MSCI emerging can be included with gold to achieve a
hedge effectiveness of 60% and 61%, respectively.

The study has many important implications that highlight the need for broader
examinations of the determinants of sustainable investment vehicles and hedging strategies
in a time of crisis. It is an important analysis for policymakers, regulators, portfolio managers,
hedgers, risk managers, investors, and mutual funds who seek to understand the risks
associated with such indices in developed and emerging economies.

The study helps to reveal how the economic impact has reverberated through multiple
channels, including commodity and financial markets, trade and migration links, and the
adverse impact on confidence exacerbated by the Russia-Ukraine war. Developing and
emerging countries are capable of implementing monetary and fiscal stimulus packages to
revive economies, stabilize financial markets, and mitigate contagion effects. They should
cooperate to understand the direction of propagation and its main causes. The implications of
stock price reactions can be compiled to provide an informative review of the future economic
impact and global energy transition.

The study encountered some limitations. Data availability was one of the main limitations as we ended up with a different set of data for the developed and the emerging indices. We recommend that future research should include more indices and shed light on the importance to combine innovative technologies, ESG metrics, and financial and commodities assets. Moreover, future research can use our findings to study the integration of regional sectoral indices with sustainable global indices. New artificial intelligence approaches, such as the advent of ChatGPT, is also important to take into account. Such technology might alleviate the burden of complicated models and simulations to choose the best investment strategies and apply hedging ratios that can account for the dynamic environment and potential crisis.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

The authors have declared that there was no use of any previously published material.

ORCID

Rim El Khoury ID http://orcid.org/0000-0003-4359-7591

Nohade Nasrallah ID http://orcid.org/0000-0003-3396-7886

Khaled Hussainey ID http://orcid.org/0000-0003-3641-1031

Rima Assaf ID http://orcid.org/0000-0002-5385-4590

ENDNOTES

¹ https://www.jpmorgan.com/insights/research/russia-ukraine-crisis-market-impact

² https://www.ecb.europa.eu/press/pressconf/2022/html/ecb.is220310~1bc8c1b1ca.en.html

³ https://npkcap.com/venture-capital-begins-to-see-the-convergence-of-fintech-and-climate-finance/

⁴ https://www.jpmorgan.com/insights/research/russia-ukraine-crisis-market-impact

⁵ https://www.worldbank.org/en/news/press-release/2022/04/10/russian-invasion-to-shrink-ukraine-economy-by-45-percent-this-year
and 83% effective, indicating the important role of MSCI and ESG in both countries to hedge
renewable energy. Similarly, ESG and MSCI emerging can be included with gold to achieve a
hedge effectiveness of 60% and 61%, respectively.

The study has many important implications that highlight the need for broader
examinations of the determinants of sustainable investment vehicles and hedging strategies
in a time of crisis. It is an important analysis for policymakers, regulators, portfolio managers,
hedgers, risk managers, investors, and mutual funds who seek to understand the risks
associated with such indices in developed and emerging economies.

The study helps to reveal how the economic impact has reverberated through multiple
channels, including commodity and financial markets, trade and migration links, and the
adverse impact on confidence exacerbated by the Russia-Ukraine war. Developing and
emerging countries are capable of implementing monetary and fiscal stimulus packages to
revive economies, stabilize financial markets, and mitigate contagion effects. They should
cooperate to understand the direction of propagation and its main causes. The implications of
stock price reactions can be compiled to provide an informative review of the future economic
impact and global energy transition.

The study encountered some limitations. Data availability was one of the main limitations as we ended up with a different set of data for the developed and the emerging indices. We recommend that future research should include more indices and shed light on the importance to combine innovative technologies, ESG metrics, and financial and commodities assets. Moreover, future research can use our findings to study the integration of regional sectoral indices with sustainable global indices. New artificial intelligence approaches, such as the advent of ChatGPT, is also important to take into account. Such technology might alleviate the burden of complicated models and simulations to choose the best investment strategies and apply hedging ratios that can account for the dynamic environment and potential crisis.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

The authors have declared that there was no use of any previously published material.

ORCID

Rim El Khoury ID http://orcid.org/0000-0003-4359-7591

Nohade Nasrallah ID http://orcid.org/0000-0003-3396-7886

Khaled Hussainey ID http://orcid.org/0000-0003-3641-1031

Rima Assaf ID http://orcid.org/0000-0002-5385-4590

ENDNOTES

¹ https://www.jpmorgan.com/insights/research/russia-ukraine-crisis-market-impact

² https://www.ecb.europa.eu/press/pressconf/2022/html/ecb.is220310~1bc8c1b1ca.en.html

³ https://npkcap.com/venture-capital-begins-to-see-the-convergence-of-fintech-and-climate-finance/

⁴ https://www.jpmorgan.com/insights/research/russia-ukraine-crisis-market-impact

⁵ https://www.worldbank.org/en/news/press-release/2022/04/10/russian-invasion-to-shrink-ukraine-economy-by-45-percent-this-year
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Chueca Vergara, C., & Ferruz Agudo, L. (2021). Fintech and sustainability: Do they affect each other? *Sustainability*, **13**(13), 7012.

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. *Administrative Science Quarterly*, **35**, 128-152.

Corbet, S., Larkin, C., & Lucey, B. (2020). The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies. *Finance Research Letters*, **35**, 101554. https://doi.org/10.1016/j.frl.2020.101554

Dawar, I., Dutta, A., Bouri, E., & Saeed, T. (2021). Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression. *Renewable Energy*, **163**, 288-299.

Deng, M., Leippold, M., Wagner, A. F., & Wang, Q. (2022). Stock prices and the Russia-Ukraine War: Sanctions, energy and ESG. *Swiss Finance Institute Research Paper*, 22-29. Available at SSRN: https://ssrn.com/abstract=4121382

Diebold, F. X., & Yilmaz, K. (2015). Trans-Atlantic equity volatility connectedness: U.S. and European financial institutions, 2004-2014. *Journal of Financial Econometrics*, **14**(1), nbv021. https://doi.org/10.1093/jjfinec/nbv021
REFERENCES

Abbassi, W., Kumari, V., & Pandey, D. K. (2023). What makes firms vulnerable to the Russia-Ukraine crisis? *The Journal of Risk Finance*, **24**(1), 24-39.

Akhtaruzzaman, M., Boubaker, S., & Sensoy, A. (2021). Financial contagion during COVID-19 crisis. *Finance Research Letters*, **38**, 101604. https://doi.org/10.1016/j.frl.2020.101604

Akhtaruzzaman, M., Boubaker, S., & Umar, Z. (2022). COVID-19 media coverage and ESG leader indices. *Finance Research Letters*, **45**, 102170. https://doi.org/10.1016/j.frl.2021.102170

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. *Journal of Risk and Financial Management*, **13**(4), 84.

Antonakakis, N., & Gabauer, D. (2017). *Refined measures of dynamic connectedness based on TVP-VAR*. https://mpra.ub.uni-muenchen.de/78282/

Apriliyanti, I. D., & Alon, I. (2017). Bibliometric analysis of absorptive capacity. *International Business Review*, **26**(5), 896-907. https://doi.org/10.1016/j.ibusrev.2017.02.007

Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. *Resources Policy*, **73**, 102219.

Banerjee, A. K. (2021). Futures market and the contagion effect of COVID-19 syndrome. *Finance Research Letters*, **43**, 102018. https://doi.org/10.1016/j.frl.2021.102018

Bashmakov, I. A., Nilsson, L. J., Acquaye, A., Bataille, C., Cullen, J. M., Fischedick, M., Geng, Y., & Tanaka, K. (2022). Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Chapter 11.

Batten, J. A., Choudhury, T., Kinateder, H., Wagner, N. F., Batten, J. A., Choudhury, T., Kinateder, H., & Wagner, N. F. (2022). Volatility impacts on the European banking sector: GFC and COVID-19. *Annals of Operations Research*, 1-26.

Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. *Economic Modelling*, **48**, 16-24.

Boubaker, S., Goodell, J. W., Pandey, D. K., & Kumari, V. (2022). Heterogeneous impacts of wars on global equity markets: Evidence from the invasion of Ukraine. *Finance Research Letters*, **48**, 102934.

Boungou, W., & Yatié, A. (2022). The impact of the Ukraine-Russia war on world stock market returns. *Economics Letters*, **215**, 110516.

Broadstock, D. C., & Cheng, L. T. W. (2019). Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. *Finance Research Letters*, **29**, 17-22. https://doi.org/10.1016/j.frl.2019.02.006

Cepoi, C. O. (2020). Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil. *Finance Research Letters*, **36**, 101658. https://doi.org/10.1016/j.frl.2020.101658

Chepeliev, M., Hertel, T. W., & van der Mensbrugghe, D. (2022). Cutting Russia's fossil fuel exports: Short-term pain for long-term gain. Available at SSRN.

Chortane, S. G., & Pandey, D. K. (2022). Does the Russia-Ukraine war lead to currency asymmetries? A US dollar tale. *The Journal of Economic Asymmetries*, **26**, e00265. https://doi.org/10.1016/j.jeca.2022.e00265

Chueca Vergara, C., & Ferruz Agudo, L. (2021). Fintech and sustainability: Do they affect each other? *Sustainability*, **13**(13), 7012.

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. *Administrative Science Quarterly*, **35**, 128-152.

Corbet, S., Larkin, C., & Lucey, B. (2020). The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies. *Finance Research Letters*, **35**, 101554. https://doi.org/10.1016/j.frl.2020.101554

Dawar, I., Dutta, A., Bouri, E., & Saeed, T. (2021). Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression. *Renewable Energy*, **163**, 288-299.

Deng, M., Leippold, M., Wagner, A. F., & Wang, Q. (2022). Stock prices and the Russia-Ukraine War: Sanctions, energy and ESG. *Swiss Finance Institute Research Paper*, 22-29. Available at SSRN: https://ssrn.com/abstract=4121382

Diebold, F. X., & Yilmaz, K. (2015). Trans-Atlantic equity volatility connectedness: U.S. and European financial institutions, 2004-2014. *Journal of Financial Econometrics*, **14**(1), nbv021. https://doi.org/10.1093/jjfinec/nbv021
Diebold, F. X., & Yilmaz, K. (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

Ederington, L. H. (1979). The hedging performance of the new futures markets. *The Journal of Finance, 34*(1), 157–170.

El Khoury, R., Nasrallah, N., Harb, E., & Hussainey, K. (2022). Exploring the performance of responsible companies in G20 during the COVID-19 outbreak. *Journal of Cleaner Production, 354*, 131693. https://doi. org/10.1016/j.jclepro.2022.131693

Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. *Journal of Business & Economic Statistics, 20*(3), 339–350.

Fernandez, V. (2008). The war on terror and its impact on the long-term volatility of financial markets. *International Review of Financial Analysis, 17*(1), 1–26.

Ferrara, L., Mogliani, M., & Sahuc, J.-G. (2022). High-frequency monitoring of growth at risk. *International Journal of Forecasting, 38*(2), 582–595.

Ferrer, R., Benítez, R., & Bolós, V. J. (2021). Interdependence between green financial instruments and major conventional assets: A wavelet-based network analysis. *Mathematics, 9*(8), 900. https://doi.org/10.3390/ math9080900

Foster-McGregor, N., Pöschl, J., & Stehrer, R. (2017). The importance of absorptive capacities: Productivity effects of international R&D spillovers through intermediate inputs. *Economics of Innovation and New Technology, 26*(8), 719–733.

Friede, G., Busch, T., & Bassen, A. (2015). {ESG} and financial performance: Aggregated evidence from more than 2000 empirical studies. *Journal of Sustainable Finance & Investment, 5*(4), 210–233. https://doi.org/10. 1080/20430795.2015.1118917

Funakoshi, M., Lawson, H., & Deka, K. (2022). Tracking sanctions against Russia. Reuters.

Goetzmann, W. N., & Kumar, A. (2005). *Why do individual investors hold under-diversified portfolios?* (Issue ysm454). Yale School of Management Working Papers.

Gu, X., Zhang, W., & Cheng, S. (2021). How do investors in Chinese stock market react to external uncertainty? An event study to the Sino-US disputes. *Pacific-Basin Finance Journal, 68*, 101614.

Hanif, W., Arreola Hernandez, J., Mensi, W., Kang, S. H., Uddin, G. S., & Yoon, S.-M. (2021). Nonlinear dependence and connectedness between clean/renewable energy sector equity and European emission allowance prices. *Energy Economics, 101*, 105409. https://doi.org/10.1016/j.eneco.2021.105409

Hassan, M. K., Boubaker, S., Kumari, V., & Pandey, D. K. (2022). Border disputes and heterogeneous sectoral returns: An event study approach. *Finance Research Letters, 50*, 103277.

Hinson, R., Lensink, R., & Mueller, A. (2019). Transforming agribusiness in developing countries: SDGs and the role of FinTech. *Current Opinion in Environmental Sustainability, 41*, 1–9.

Huynh, T. L. D., Hille, E., & Nasir, M. A. (2020). Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies. *Technological Forecasting and Social Change, 159*, 120188.

Kinateder, H., Campbell, R., & Choudhury, T. (2021). Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets. *Finance Research Letters, 43*, 101951. https://doi.org/10.1016/j.frl.2021.101951

Klein, T., Pham Thu, H., & Walther, T. (2018). Bitcoin is not the new gold—A comparison of volatility, correlation, and portfolio performance. *International Review of Financial Analysis, 59*, 105–116.

Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. *Journal of Econometrics, 74*(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4

Koutsokostas, D., Papathanasiou, S., & Balios, D. (2019). Adjusting for risk factors in mutual fund performance and performance persistence. *The Journal of Risk Finance, 20*(4), 352–369. https://doi.org/10.1108/JRF-07- 2018-0108

Kroner, K. F., & Ng, V. K. (1998). Modeling asymmetric comovements of asset returns. *Review of Financial Studies, 11*(4), 817–844.

Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. *The Journal of Financial and Quantitative Analysis, 28*(4), 535–551.
Diebold, F. X., & Yilmaz, K. (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

Ederington, L. H. (1979). The hedging performance of the new futures markets. *The Journal of Finance, 34*(1), 157–170.

El Khoury, R., Nasrallah, N., Harb, E., & Hussainey, K. (2022). Exploring the performance of responsible companies in G20 during the COVID-19 outbreak. *Journal of Cleaner Production, 354*, 131693. https://doi. org/10.1016/j.jclepro.2022.131693

Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. *Journal of Business & Economic Statistics, 20*(3), 339–350.

Fernandez, V. (2008). The war on terror and its impact on the long-term volatility of financial markets. *International Review of Financial Analysis, 17*(1), 1–26.

Ferrara, L., Mogliani, M., & Sahuc, J.-G. (2022). High-frequency monitoring of growth at risk. *International Journal of Forecasting, 38*(2), 582–595.

Ferrer, R., Benítez, R., & Bolós, V. J. (2021). Interdependence between green financial instruments and major conventional assets: A wavelet-based network analysis. *Mathematics, 9*(8), 900. https://doi.org/10.3390/ math9080900

Foster-McGregor, N., Pöschl, J., & Stehrer, R. (2017). The importance of absorptive capacities: Productivity effects of international R&D spillovers through intermediate inputs. *Economics of Innovation and New Technology, 26*(8), 719–733.

Friede, G., Busch, T., & Bassen, A. (2015). {ESG} and financial performance: Aggregated evidence from more than 2000 empirical studies. *Journal of Sustainable Finance & Investment, 5*(4), 210–233. https://doi.org/10. 1080/20430795.2015.1118917

Funakoshi, M., Lawson, H., & Deka, K. (2022). Tracking sanctions against Russia. Reuters.

Goetzmann, W. N., & Kumar, A. (2005). *Why do individual investors hold under-diversified portfolios?* (Issue ysm454). Yale School of Management Working Papers.

Gu, X., Zhang, W., & Cheng, S. (2021). How do investors in Chinese stock market react to external uncertainty? An event study to the Sino-US disputes. *Pacific-Basin Finance Journal, 68*, 101614.

Hanif, W., Arreola Hernandez, J., Mensi, W., Kang, S. H., Uddin, G. S., & Yoon, S.-M. (2021). Nonlinear dependence and connectedness between clean/renewable energy sector equity and European emission allowance prices. *Energy Economics, 101*, 105409. https://doi.org/10.1016/j.eneco.2021.105409

Hassan, M. K., Boubaker, S., Kumari, V., & Pandey, D. K. (2022). Border disputes and heterogeneous sectoral returns: An event study approach. *Finance Research Letters, 50*, 103277.

Hinson, R., Lensink, R., & Mueller, A. (2019). Transforming agribusiness in developing countries: SDGs and the role of FinTech. *Current Opinion in Environmental Sustainability, 41*, 1–9.

Huynh, T. L. D., Hille, E., & Nasir, M. A. (2020). Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies. *Technological Forecasting and Social Change, 159*, 120188.

Kinateder, H., Campbell, R., & Choudhury, T. (2021). Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets. *Finance Research Letters, 43*, 101951. https://doi.org/10.1016/j.frl.2021.101951

Klein, T., Pham Thu, H., & Walther, T. (2018). Bitcoin is not the new gold—A comparison of volatility, correlation, and portfolio performance. *International Review of Financial Analysis, 59*, 105–116.

Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. *Journal of Econometrics, 74*(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4

Koutsokostas, D., Papathanasiou, S., & Balios, D. (2019). Adjusting for risk factors in mutual fund performance and performance persistence. *The Journal of Risk Finance, 20*(4), 352–369. https://doi.org/10.1108/JRF-07- 2018-0108

Kroner, K. F., & Ng, V. K. (1998). Modeling asymmetric comovements of asset returns. *Review of Financial Studies, 11*(4), 817–844.

Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. *The Journal of Financial and Quantitative Analysis, 28*(4), 535–551.
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Li, J., Li, J., Zhu, X., Yao, Y., & Casu, B. (2020). Risk spillovers between FinTech and traditional financial institutions: Evidence from the US. *International Review of Financial Analysis*, **71**, 101544.

Li, Y., & Giles, D. E. (2015). Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets. *International Journal of Finance & Economics*, **20**(2), 155-177.

Li, Z., & Meng, Q. (2022). Time and frequency connectedness and portfolio diversification between cryptocurrencies and renewable energy stock markets during COVID-19. *The North American Journal of Economics and Finance*, **59**, 101565. https://doi.org/10.1016/j.najef.2021.101565

Liu, N., Liu, C., Da, B., Zhang, T., & Guan, F. (2021). Dependence and risk spillovers between green bonds and clean energy markets. *Journal of Cleaner Production*, **279**, 123595.

Lo, G.-D., Marcelin, I., Bassène, T., & Sène, B. (2022). The Russo-Ukrainian war and financial markets: The role of dependence on Russian commodities. *Finance Research Letters*, **50**, 103194.

Lucey, B. M., & Li, S. (2015). What precious metals act as safe havens, and when? Some US evidence. *Applied Economics Letters*, **22**(1), 35-45.

Magazzino, C., Mutascu, M., Mele, M., & Sarkodie, S. A. (2021). Energy consumption and economic growth in Italy: A wavelet analysis. *Energy Reports*, **7**, 1520-1528.

Mathis, W., & Wade, W. (2022). *Clean-energy stocks surge as war spurs push away from Russia*. www.bloomberg.com/news/articles/2022-02-28/wind-energy-stocks-surge-as-war-spurs-push-away-from-russia

Mokni, K., & Mansouri, F. (2017). Conditional dependence between international stock markets: A long memory GARCH-copula model approach. *Journal of Multinational Financial Management*, **42-43**, 116-131.

Naeem, M. A., Peng, Z., Suleman, M. T., Nepal, R., & Shahzad, S. J. H. (2020). Time and frequency connectedness among oil shocks, electricity and clean energy markets. *Energy Economics*, **91**, 104914. https://doi.org/10.1016/j.eneco.2020.104914

Naeem, M. A., Farid, S., Ferrer, R., & Shahzad, S. J. H. (2021). Comparative efficiency of green and conventional bonds pre-and during COVID-19: An asymmetric multifractal detrended fluctuation analysis. *Energy Policy*, **153**, 112285.

Nakai, M., Yamaguchi, K., & Takeuchi, K. (2016). Can SRI funds better resist global financial crisis? Evidence from Japan. *International Review of Financial Analysis*, **48**, 12-20.

Nerlinger, M., & Utz, S. (2022). The impact of the Russia-Ukraine conflict on energy firms: A capital market perspective. *Finance Research Letters*, **50**, 103243.

Nguyen, T. T. H., Naeem, M. A., Balli, F., Balli, H. O., & Vo, X. V. (2021). Time-frequency comovement among green bonds, stocks, commodities, clean energy, and conventional bonds. *Finance Research Letters*, **40**, 101739. https://doi.org/10.1016/j.frl.2020.101739

Pandey, D. K., & Kumar, R. (2022). Russia-Ukraine War and the global tourism sector: A 13-day tale. *Current Issues in Tourism*, **26**(5), 692-700.

Pástor, L., & Vorsatz, M. B. (2020). Mutual fund performance and flows during the COVID-19 crisis. *The Review of Asset Pricing Studies*, **10**(4), 791-833.

Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. *Economic Modelling*, **88**, 25-38. https://doi.org/10.1016/j.econmod.2019.09.004

Reboredo, J. C., Ugolini, A., & Aiube, F. A. L. (2020). Network connectedness of green bonds and asset classes. *Energy Economics*, **86**, 104629. https://doi.org/10.1016/j.eneco.2019.104629

Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. *Energy Economics*, **74**, 38-50.

Sachs, J. D., Woo, W. T., Yoshino, N., & Taghizadeh-Hesary, F. (2019). Importance of green finance for achieving sustainable development goals and energy security, *Handbook of green finance* (pp. 3-12). Springer.

Saeed, T., Bouri, E., & Alsulami, H. (2021). Extreme return connectedness and its determinants between clean/ green and dirty energy investments. *Energy Economics*, **96**, 105017. https://doi.org/10.1016/j.eneco.2020.105017

Schneider, G., & Troeger, V. E. (2006). War and the world economy: Stock market reactions to international conflicts. *Journal of Conflict Resolution*, **50**(5), 623-645.
Le, T. L., Abakah, E. J. A., & Tiwari, A. K. (2021). Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution. *Technological Forecasting and Social Change*, **162**, 120382.

Li, J., Li, J., Zhu, X., Yao, Y., & Casu, B. (2020). Risk spillovers between FinTech and traditional financial institutions: Evidence from the US. *International Review of Financial Analysis*, **71**, 101544.

Li, Y., & Giles, D. E. (2015). Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets. *International Journal of Finance & Economics*, **20**(2), 155-177.

Li, Z., & Meng, Q. (2022). Time and frequency connectedness and portfolio diversification between cryptocurrencies and renewable energy stock markets during COVID-19. *The North American Journal of Economics and Finance*, **59**, 101565. https://doi.org/10.1016/j.najef.2021.101565

Liu, N., Liu, C., Da, B., Zhang, T., & Guan, F. (2021). Dependence and risk spillovers between green bonds and clean energy markets. *Journal of Cleaner Production*, **279**, 123595.

Lo, G.-D., Marcelin, I., Bassène, T., & Sène, B. (2022). The Russo-Ukrainian war and financial markets: The role of dependence on Russian commodities. *Finance Research Letters*, **50**, 103194.

Lucey, B. M., & Li, S. (2015). What precious metals act as safe havens, and when? Some US evidence. *Applied Economics Letters*, **22**(1), 35-45.

Magazzino, C., Mutascu, M., Mele, M., & Sarkodie, S. A. (2021). Energy consumption and economic growth in Italy: A wavelet analysis. *Energy Reports*, **7**, 1520-1528.

Mathis, W., & Wade, W. (2022). *Clean-energy stocks surge as war spurs push away from Russia*. www.bloomberg.com/news/articles/2022-02-28/wind-energy-stocks-surge-as-war-spurs-push-away-from-russia

Mokni, K., & Mansouri, F. (2017). Conditional dependence between international stock markets: A long memory GARCH-copula model approach. *Journal of Multinational Financial Management*, **42-43**, 116-131.

Naeem, M. A., Peng, Z., Suleman, M. T., Nepal, R., & Shahzad, S. J. H. (2020). Time and frequency connectedness among oil shocks, electricity and clean energy markets. *Energy Economics*, **91**, 104914. https://doi.org/10.1016/j.eneco.2020.104914

Naeem, M. A., Farid, S., Ferrer, R., & Shahzad, S. J. H. (2021). Comparative efficiency of green and conventional bonds pre-and during COVID-19: An asymmetric multifractal detrended fluctuation analysis. *Energy Policy*, **153**, 112285.

Nakai, M., Yamaguchi, K., & Takeuchi, K. (2016). Can SRI funds better resist global financial crisis? Evidence from Japan. *International Review of Financial Analysis*, **48**, 12-20.

Nerlinger, M., & Utz, S. (2022). The impact of the Russia-Ukraine conflict on energy firms: A capital market perspective. *Finance Research Letters*, **50**, 103243.

Nguyen, T. T. H., Naeem, M. A., Balli, F., Balli, H. O., & Vo, X. V. (2021). Time-frequency comovement among green bonds, stocks, commodities, clean energy, and conventional bonds. *Finance Research Letters*, **40**, 101739. https://doi.org/10.1016/j.frl.2020.101739

Pandey, D. K., & Kumar, R. (2022). Russia-Ukraine War and the global tourism sector: A 13-day tale. *Current Issues in Tourism*, **26**(5), 692-700.

Pástor, L., & Vorsatz, M. B. (2020). Mutual fund performance and flows during the COVID-19 crisis. *The Review of Asset Pricing Studies*, **10**(4), 791-833.

Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. *Economic Modelling*, **88**, 25-38. https://doi.org/10.1016/j.econmod.2019.09.004

Reboredo, J. C., Ugolini, A., & Aiube, F. A. L. (2020). Network connectedness of green bonds and asset classes. *Energy Economics*, **86**, 104629. https://doi.org/10.1016/j.eneco.2019.104629

Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. *Energy Economics*, **74**, 38-50.

Sachs, J. D., Woo, W. T., Yoshino, N., & Taghizadeh-Hesary, F. (2019). Importance of green finance for achieving sustainable development goals and energy security, *Handbook of green finance* (pp. 3-12). Springer.

Saeed, T., Bouri, E., & Alsulami, H. (2021). Extreme return connectedness and its determinants between clean/ green and dirty energy investments. *Energy Economics*, **96**, 105017. https://doi.org/10.1016/j.eneco.2020.105017

Schneider, G., & Troeger, V. E. (2006). War and the world economy: Stock market reactions to international conflicts. *Journal of Conflict Resolution*, **50**(5), 623-645.
Sharma, G. D., Sarker, T., Rao, A., Talan, G., & Jain, M. (2022). Revisiting conventional and green finance spillover in post-COVID world: Evidence from robust econometric models. *Global Finance Journal*, *51*, 100691.

Sharma, G. D., Tiwari, A. K., Talan, G., & Jain, M. (2021). Revisiting the sustainable versus conventional investment dilemma in COVID-19 times. *Energy Policy*, *156*, 112467.

Sklar, A. (1973). Random variables, joint distribution functions, and copulas. *Kybernetika*, *9*(6), 449–460.

Taghizadeh-Hesary, F., & Yoshino, N. (2019). The way to induce private participation in green finance and investment. *Finance Research Letters*, *31*, 98–103.

Taghizadeh-Hesary, F., Yoshino, N., Rasoulinezhad, E., & Rimaud, C. (2021). Power purchase agreements with incremental tariffs in local currency: An innovative green finance tool. *Global Finance Journal*, *50*, 100666.

Thampanya, N., Nasir, M. A., & Huynh, T. (2020). Asymmetric correlation and hedging effectiveness of gold & cryptocurrencies: From pre-industrial to the 4th industrial revolution. *Technological Forecasting and Social Change*, *159*, 120195.

Theiri, S., Nekhili, R., & Sultan, J. (2023). Cryptocurrency liquidity during the Russia–Ukraine war: The case of Bitcoin and Ethereum. *The Journal of Risk Finance*, *24*(1), 59–71.

Tosun, O. K., & Eshraghi, A. (2022). Corporate decisions in times of war: Evidence from the Russia-Ukraine conflict. *Finance Research Letters*, *48*, 102920.

Umar, Z., Polat, O., Choi, S.-Y., & Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. *Finance Research Letters*, *48*, 102976.

Urquhart, A., & Zhang, H. (2019). Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. *International Review of Financial Analysis*, *63*, 49–57. https://doi.org/10.1016/j.irfa.2019.02.009

Xia, T., Ji, Q., Zhang, D., & Han, J. (2019). Asymmetric and extreme influence of energy price changes on renewable energy stock performance. *Journal of Cleaner Production*, *241*, 118338.

Yao, M., Di, H., Zheng, X., & Xu, X. (2018). Impact of payment technology innovations on the traditional financial industry: A focus on China. *Technological Forecasting and Social Change*, *135*, 199–207.

Yarovaya, L., & Mirza, N. (2022). The price reaction and investment exposure of equity funds: Evidence from the Russia—Ukraine military conflict. *The Journal of Risk Finance*, *23*, 669–676.

Yousaf, I., Patel, R., & Yarovaya, L. (2022). The reaction of G20+ stock markets to the Russia–Ukraine conflict “black-swan” event: Evidence from event study approach. *Journal of Behavioral and Experimental Finance*, *35*, 100723.

Zavyalova, E., Studenikin, N., & Starikova, E. (2018). Business participation in implementation of socially oriented sustainable development goals in countries of Central Asia and the Caucasus region. *Central Asia and the Caucasus*, *19*(2), 56–63.

Zhang, W., Zhuang, X., Wang, J., & Lu, Y. (2020). Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network. *The North American Journal of Economics and Finance*, *54*, 101248. https://doi.org/10.1016/j.najef.2020.101248

Zhang, Z., Hung, K., & Chang, T. (2017). P2P loans and bank loans, the chicken and the egg, what causes what?: Further evidence from a bootstrap panel granger causality test. *Applied Economics Letters*, *24*(19), 1358–1362.

**How to cite this article:** El Khoury, R., Nasrallah, N., Hussainey, K., & Assaf, R. (2023). Spillover analysis across FinTech, ESG, and renewable energy indices before and during the Russia–Ukraine war: International evidence. *Journal of International Financial Management & Accounting*, *34*, 279–317. https://doi.org/10.1111/jifm.12179
Sharma, G. D., Sarker, T., Rao, A., Talan, G., & Jain, M. (2022). Revisiting conventional and green finance spillover in post-COVID world: Evidence from robust econometric models. *Global Finance Journal*, *51*, 100691.

Sharma, G. D., Tiwari, A. K., Talan, G., & Jain, M. (2021). Revisiting the sustainable versus conventional investment dilemma in COVID-19 times. *Energy Policy*, *156*, 112467.

Sklar, A. (1973). Random variables, joint distribution functions, and copulas. *Kybernetika*, *9*(6), 449–460.

Taghizadeh-Hesary, F., & Yoshino, N. (2019). The way to induce private participation in green finance and investment. *Finance Research Letters*, *31*, 98–103.

Taghizadeh-Hesary, F., Yoshino, N., Rasoulinezhad, E., & Rimaud, C. (2021). Power purchase agreements with incremental tariffs in local currency: An innovative green finance tool. *Global Finance Journal*, *50*, 100666.

Thampanya, N., Nasir, M. A., & Huynh, T. (2020). Asymmetric correlation and hedging effectiveness of gold & cryptocurrencies: From pre-industrial to the 4th industrial revolution. *Technological Forecasting and Social Change*, *159*, 120195.

Theiri, S., Nekhili, R., & Sultan, J. (2023). Cryptocurrency liquidity during the Russia–Ukraine war: The case of Bitcoin and Ethereum. *The Journal of Risk Finance*, *24*(1), 59–71.

Tosun, O. K., & Eshraghi, A. (2022). Corporate decisions in times of war: Evidence from the Russia-Ukraine conflict. *Finance Research Letters*, *48*, 102920.

Umar, Z., Polat, O., Choi, S.-Y., & Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. *Finance Research Letters*, *48*, 102976.

Urquhart, A., & Zhang, H. (2019). Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. *International Review of Financial Analysis*, *63*, 49–57. https://doi.org/10.1016/j.irfa.2019.02.009

Xia, T., Ji, Q., Zhang, D., & Han, J. (2019). Asymmetric and extreme influence of energy price changes on renewable energy stock performance. *Journal of Cleaner Production*, *241*, 118338.

Yao, M., Di, H., Zheng, X., & Xu, X. (2018). Impact of payment technology innovations on the traditional financial industry: A focus on China. *Technological Forecasting and Social Change*, *135*, 199–207.

Yarovaya, L., & Mirza, N. (2022). The price reaction and investment exposure of equity funds: Evidence from the Russia—Ukraine military conflict. *The Journal of Risk Finance*, *23*, 669–676.

Yousaf, I., Patel, R., & Yarovaya, L. (2022). The reaction of G20+ stock markets to the Russia–Ukraine conflict “black-swan” event: Evidence from event study approach. *Journal of Behavioral and Experimental Finance*, *35*, 100723.

Zavyalova, E., Studenikin, N., & Starikova, E. (2018). Business participation in implementation of socially oriented sustainable development goals in countries of Central Asia and the Caucasus region. *Central Asia and the Caucasus*, *19*(2), 56–63.

Zhang, W., Zhuang, X., Wang, J., & Lu, Y. (2020). Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network. *The North American Journal of Economics and Finance*, *54*, 101248. https://doi.org/10.1016/j.najef.2020.101248

Zhang, Z., Hung, K., & Chang, T. (2017). P2P loans and bank loans, the chicken and the egg, what causes what?: Further evidence from a bootstrap panel granger causality test. *Applied Economics Letters*, *24*(19), 1358–1362.

**How to cite this article:** El Khoury, R., Nasrallah, N., Hussainey, K., & Assaf, R. (2023). Spillover analysis across FinTech, ESG, and renewable energy indices before and during the Russia–Ukraine war: International evidence. *Journal of International Financial Management & Accounting*, *34*, 279–317. https://doi.org/10.1111/jifm.12179