The impact of conventional energy markets on connectedness dynamics among eco-friendly assets

References

• Abakah, E. J. A., Ullah, G. W., Adekoya, O. B., Bonsu, C. O., & Abdullah, M. (2023). Blockchain market and eco-friendly financial assets: Dynamic price correlation, connectedness and spillovers with portfolio implications. International Review of Economics & Finance, 87, 218–243. https://doi.org/10.1016/j.iref.2023.04.028
   Web of Science ®Google Scholar
• Adebayo, T. S., & Kartal, M. T. (2023). Effect of green bonds, oil prices, and COVID-19 on industrial CO2 emissions in the USA: Evidence from novel wavelet local multiple correlation approach. Energy & Environment, 0958305X231167463. https://doi.org/10.1177/0958305X231167463
   Web of Science ®Google Scholar
• Ahmad, W., Sadorsky, P., & Sharma, A. (2018). Optimal hedge ratios for clean energy equities. Economic Modelling, 72, 278–295. https://doi.org/10.1016/j.econmod.2018.02.008
   Web of Science ®Google Scholar
• Alola, A. A., Özkan, O., & Obekpa, H. O. (2023). Examining the patterns of disaggregate energy security risk and crude oil price: The USA scenario over 1970–2040. Resources Policy, 82, 103514. https://doi.org/10.1016/j.resourpol.2023.103514
   Web of Science ®Google Scholar
• Alola, A. A., Özkan, O., & Usman, O. (2023). Examining crude oil price outlook amidst substitute energy price and household energy expenditure in the USA: A novel nonparametric multivariate QQR approach. Energy Economics, 120, 106613. https://doi.org/10.1016/j.eneco.2023.106613
   Web of Science ®Google Scholar
• Anscombe, F. J., & Glynn, W. J. (1983). Distribution of the kurtosis statistic b2 for normal samples. Biometrika, 70(1), 227–234. https://doi.org/10.1093/biomet/70.1.227
   Web of Science ®Google Scholar
• Antonakakis, N., Gabauer, D., & Gupta, R. (2019). International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression. International Review of Financial Analysis, 65, 101382. https://doi.org/10.1016/j.irfa.2019.101382
   Web of Science ®Google Scholar
• Azhgaliyeva, D., Kapsalyamova, Z., & Mishra, R. (2022). Oil price shocks and green bonds: An empirical evidence. Energy Economics, 112, 106108. https://doi.org/10.1016/j.eneco.2022.106108
   Web of Science ®Google Scholar
• Azimli, A. (2020). The impact of COVID-19 on the degree of dependence and structure of risk-return relationship: A quantile regression approach. Finance Research Letters, 36, 101648. https://doi.org/10.1016/j.frl.2020.101648
   PubMed Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.resourpol.2021.102219
   Web of Science ®Google Scholar
• Balcilar, M., Gupta, R., & Pierdzioch, C. (2016). Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resources Policy, 49, 74–80. https://doi.org/10.1016/j.resourpol.2016.04.004
   Web of Science ®Google Scholar
• Bouoiyour, J., Gauthier, M., & Bouri, E. (2023). Which is leading: Renewable or brown energy assets? Energy Economics, 117, 106339. https://doi.org/10.1016/j.eneco.2022.106339
   Web of Science ®Google Scholar
• Bouri, E. (2023). Spillovers in the joint system of conditional higher-order moments: US evidence from green energy, brown energy, and technology stocks. Renewable Energy, 210, 507–523. https://doi.org/10.1016/j.renene.2023.04.006
   Web of Science ®Google Scholar
• Chatziantoniou, I., Abakah, E. J. A., Gabauer, D., & Tiwari, A. K. (2022). Quantile time–frequency price connectedness between green bond, green equity, sustainable investments and clean energy markets. Journal of Cleaner Production, 361, 132088. https://doi.org/10.1016/j.jclepro.2022.132088
   Web of Science ®Google Scholar
• Chatziantoniou, I., Gabauer, D., & Stenfors, A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Economics Letters, 204, 109891. https://doi.org/10.1016/j.econlet.2021.109891
   Web of Science ®Google Scholar
• Chen, S., Bouteska, A., Sharif, T., & Abedin, M. Z. (2023). The Russia–Ukraine war and energy market volatility: A novel application of the volatility ratio in the context of natural gas. Resources Policy, 85, 103792. https://doi.org/10.1016/j.resourpol.2023.103792
   Web of Science ®Google Scholar
• Coskun, M. (2023). Dynamic correlations and volatility spillovers between subsectoral clean-energy stocks and commodity futures markets: A hedging perspective. Journal of Futures Markets, 43(12), 1727–1749. https://doi.org/10.1002/fut.22454
   Web of Science ®Google Scholar
• D’Agostino, R. B. (1970). Transformation to normality of the null distribution of g1. Biometrika, 57(3), 679–681. https://doi.org/10.1093/biomet/57.3.679
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.renene.2020.08.162
   Web of Science ®Google Scholar
• Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Diebold, F. X., & Yılmaz, K. (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
   Web of Science ®Google Scholar
• Dutta, A., Bouri, E., Rothovius, T., & Uddin, G. S. (2023). Climate risk and green investments: New evidence. Energy, 265, 126376. https://doi.org/10.1016/j.energy.2022.126376
   Web of Science ®Google Scholar
• Dutta, A., Jana, R. K., & Das, D. (2020). Do green investments react to oil price shocks? Implications for sustainable development. Journal of Cleaner Production, 266, 121956. https://doi.org/10.1016/j.jclepro.2020.121956
   Web of Science ®Google Scholar
• Egiyi, M. A., & Ofoegbu, G. N. (2020). Cryptocurrency and climate change: An overview. International Journal of Mechanical Engineering and Technology, 11(3). https://doi.org/10.34218/ijmet.11.3.2020.003
   Google Scholar
• Elliott, G., Rothenberg, T. J., & Stock, J. H. (1992). Efficient tests for an autoregressive unit root (Working Paper 130). Cambridge, MA: National Bureau of Economic Research.
   Google Scholar
• Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). Cryptocurrency trading: A comprehensive survey. Financial Innovation, 8(1), Article 13. https://doi.org/10.1186/s40854-021-00321-6
   Web of Science ®Google Scholar
• Farid, S., Karim, S., Naeem, M. A., Nepal, R., & Jamasb, T. (2023). Co-movement between dirty and clean energy: A time-frequency perspective. Energy Economics, 119, 106565. https://doi.org/10.1016/j.eneco.2023.106565
   Web of Science ®Google Scholar
• Ferrer, R., Shahzad, S. J. H., López, R., & Jareño, F. (2018). Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76, 1–20. https://doi.org/10.1016/j.eneco.2018.09.022
   Web of Science ®Google Scholar
• Fisher, T. J., & Gallagher, C. M. (2012). New weighted portmanteau statistics for time series goodness of fit testing. Journal of the American Statistical Association, 107(498), 777–787. https://doi.org/10.1080/01621459.2012.688465
   Web of Science ®Google Scholar
• Foglia, M., Angelini, E., & Huynh, T. L. D. (2022). Tail risk connectedness in clean energy and oil financial market. Annals of Operations Research. 1–25. Retrieved from https://doi.org/10.1007/s10479-022-04745-w
   PubMed Web of Science ®Google Scholar
• Gabauer, D., Chatziantoniou, I., & Stenfors, A. (2023). Model-free connectedness measures. Finance Research Letters, 54, 103804. https://doi.org/10.1016/j.frl.2023.103804
   Web of Science ®Google Scholar
• Ghaemi Asl, M., Rashidi, M. M., Tiwari, A. K., Lee, C.-C., & Roubaud, D. (2023). Green bond vs. Islamic bond: Which one is more environmentally friendly? Journal of Environmental Management, 345, 118580. https://doi.org/10.1016/j.jenvman.2023.118580
   PubMed Web of Science ®Google Scholar
• Hainmueller, J., & Hazlett, C. (2014). Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach. Political Analysis, 22(2), 143–168. https://doi.org/10.1093/pan/mpt019
   Web of Science ®Google Scholar
• Hammoudeh, S., Mokni, K., Ben-Salha, O., & Ajmi, A. N. (2021). Distributional predictability between oil prices and renewable energy stocks: Is there a role for the COVID-19 pandemic? Energy Economics, 103, 105512. https://doi.org/10.1016/j.eneco.2021.105512
   Web of Science ®Google Scholar
• Han, H., Linton, O., Oka, T., & Whang, Y.-J. (2016). The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251–270. https://doi.org/10.1016/j.jeconom.2016.03.001
   Web of Science ®Google Scholar
• Hansen, B. E. (1997). Approximate asymptotic P values for structural-change tests. Journal of Business & Economic Statistics, 15(1), 60–67. https://doi.org/10.2307/1392074
   Web of Science ®Google Scholar
• Hasan, M., Rashid, M. M., Hossain, M. N., Rahman, M. M., & Amin, M. R. (2023). Using green and ESG assets to achieve post-COVID-19 environmental sustainability. Fulbright Review of Economics and Policy, 3(1), 25–48. https://doi.org/10.1108/FREP-04-2022-0026
   Google Scholar
• Henriques, I., & Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics, 30(3), 998–1010. https://doi.org/10.1016/j.eneco.2007.11.001
   Web of Science ®Google Scholar
• Huang, J., Chen, B., Xu, Y., & Xia, X. (2023). Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A Novel TVP-VAR frequency connectedness approach. Finance Research Letters, 53, 103634. https://doi.org/10.1016/j.frl.2023.103634
   PubMed Web of Science ®Google Scholar
• Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
   Web of Science ®Google Scholar
• Jeong, K., Härdle, W. K., & Song, S. (2012). A consistent nonparametric test for causality in quantile. [h]. Econometric Theory, 28(4), 861–887. https://doi.org/10.1017/S0266466611000685
   Web of Science ®Google Scholar
• Kanamura, T. (2020). Are green bonds environmentally friendly and good performing assets? Energy Economics, 88, 104767. https://doi.org/10.1016/j.eneco.2020.104767
   Web of Science ®Google Scholar
• Kanamura, T. (2022). A model of price correlations between clean energy indices and energy commodities. Journal of Sustainable Finance & Investment, 12(2), 319–359. https://doi.org/10.1080/20430795.2020.1753434
   Web of Science ®Google Scholar
• Karim, S., Naeem, M. A., Shafiullah, M., Lucey, B. M., & Ashraf, S. (2023). Asymmetric relationship between climate policy uncertainty and energy metals: Evidence from cross-quantilogram. Finance Research Letters, 54, 103728. https://doi.org/10.1016/j.frl.2023.103728
   Web of Science ®Google Scholar
• Koenker, R., & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33–50. https://doi.org/10.2307/1913643
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Kuang, W. (2021). Are clean energy assets a safe haven for international equity markets? Journal of Cleaner Production, 302, 127006. https://doi.org/10.1016/j.jclepro.2021.127006
   Web of Science ®Google Scholar
• Lee, C.-C., Lee, C.-C., & Li, Y.-Y. (2021). Oil price shocks, geopolitical risks, and green bond market dynamics. The North American Journal of Economics and Finance, 55, 101309. https://doi.org/10.1016/j.najef.2020.101309
   Web of Science ®Google Scholar
• Li, C., Lin, S., Sun, Y., Afshan, S., & Yaqoob, T. (2022). The asymmetric effect of oil price, news-based uncertainty, and COVID-19 pandemic on equity market. Resources Policy, 77, 102740. https://doi.org/10.1016/j.resourpol.2022.102740
   PubMed Web of Science ®Google Scholar
• Liang, Y., Saner, C. B., Kwang Lim, B. M., Hong, K. T., Chong Lim, J. W., Hwee Ho, K. J., Lim, L. Z., & Loh, Y. Y. (2022). Sustainable energy-based cryptocurrency mining. In 2022 IEEE PES Innovative Smart Grid Technologies – Asia (ISGT Asia), 789–793. Singapore. https://doi.org/10.1109/ISGTAsia54193.2022.10003517
   Google Scholar
• Lucey, B., & Ren, B. (2023). Time-varying tail risk connectedness among sustainability-related products and fossil energy investments. Energy Economics, 126, 106812. https://doi.org/10.1016/j.eneco.2023.106812
   Web of Science ®Google Scholar
• Lundgren, A. I., Milicevic, A., Uddin, G. S., & Kang, S. H. (2018). Connectedness network and dependence structure mechanism in green investments. Energy Economics, 72, 145–153. https://doi.org/10.1016/j.eneco.2018.04.015
   Web of Science ®Google Scholar
• Naeem, M. A., Gul, R., Farid, S., Karim, S., & Lucey, B. M. (2023). Assessing linkages between alternative energy markets and cryptocurrencies. Journal of Economic Behavior & Organization, 211, 513–529. https://doi.org/10.1016/j.jebo.2023.04.035
   Web of Science ®Google Scholar
• Naeem, M. A., Pham, L., Senthilkumar, A., & Karim, S. (2022). Oil shocks and BRIC markets: Evidence from extreme quantile approach. Energy Economics, 108, 105932. https://doi.org/10.1016/j.eneco.2022.105932
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Managi, S., & Okimoto, T. (2013). Does the price of oil interact with clean energy prices in the stock market? Japan and the World Economy, 27, 1–9. https://doi.org/10.1016/j.japwor.2013.03.003
   Web of Science ®Google Scholar
• Mandaci, P. E., Azimli, A., & Mandaci, N. (2023). The impact of geopolitical risks on connectedness among natural resource commodities: A quantile vector autoregressive approach. Resources Policy, 85, 103957. https://doi.org/10.1016/j.resourpol.2023.103957
   Web of Science ®Google Scholar
• Nishiyama, Y., Hitomi, K., Kawasaki, Y., & Jeong, K. (2011). A consistent nonparametric test for nonlinear causality—Specification in time series regression. Journal of Econometrics, 165(1), 112–127. https://doi.org/10.1016/j.jeconom.2011.05.010
   Web of Science ®Google Scholar
• Olasehinde-Williams, G., Özkan, O., & Akadiri, S. S. (2023). Effects of climate policy uncertainty on sustainable investment: A dynamic analysis for the U.S. Environmental Science and Pollution Research International, 30(19), 55326–55339. https://doi.org/10.1007/s11356-023-26257-1
   PubMed Web of Science ®Google Scholar
• Özkan, O., Alola, A. A., & Adebayo, T. S. (2023). Environmental benefits of nonrenewable energy efficiency and renewable energy intensity in the USA and EU: Examining the role of clean technologies. Sustainable Energy Technologies and Assessments, 58, 103315. https://doi.org/10.1016/j.seta.2023.103315
   Web of Science ®Google Scholar
• Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
   Web of Science ®Google Scholar
• Pham, L. (2019). Do all clean energy stocks respond homogeneously to oil price? Energy Economics, 81, 355–379. https://doi.org/10.1016/j.eneco.2019.04.010
   Web of Science ®Google Scholar
• Pham, L., & Do, H. X. (2022). Green bonds and implied volatilities: Dynamic causality, spillovers, and implications for portfolio management. Energy Economics, 112, 106106. https://doi.org/10.1016/j.eneco.2022.106106
   Web of Science ®Google Scholar
• Pham, L., Huynh, T. L. D., & Hanif, W. (2022). Cryptocurrency, green and fossil fuel investments . https://doi.org/10.2139/ssrn.3925844
   Google Scholar
• Phillips, P. C. B., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
   Web of Science ®Google Scholar
• Piñeiro-Chousa, J., López-Cabarcos, M. Á., Sevic, A., & González-López, I. (2022). A preliminary assessment of the performance of DeFi cryptocurrencies in relation to other financial assets, volatility, and user-generated content. Technological Forecasting and Social Change, 181, 121740. https://doi.org/10.1016/j.techfore.2022.121740
   Web of Science ®Google Scholar
• Rao, A., Lucey, B., Kumar, S., & Lim, W. M. (2023). Do green energy markets catch cold when conventional energy markets sneeze? Energy Economics, 127, 107035. https://doi.org/10.1016/j.eneco.2023.107035
   Web of Science ®Google Scholar
• Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy stock prices? Energy Economics, 48, 32–45. https://doi.org/10.1016/j.eneco.2014.12.009
   Web of Science ®Google Scholar
• Ren, B., & Lucey, B. (2022). 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Shah, I. H., Hiles, C., & Morley, B. (2018). How do oil prices, macroeconomic factors and policies affect the market for renewable energy? Applied Energy, 215, 87–97. https://doi.org/10.1016/j.apenergy.2018.01.084
   Web of Science ®Google Scholar
• Sharif, A., Brahim, M., Dogan, E., & Tzeremes, P. (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
   Web of Science ®Google Scholar
• Sharma, A., Tiwari, A. K., Abakah, E. J. A., & Owusu, F. B. (2023). A cross-quantile correlation and causality-in-quantile analysis on the relationship between green investments and energy commodities during the COVID-19 pandemic period. Studies in Economics and Finance, ahead-of-print(ahead-of-print).
   Google Scholar
• Sibande, X., Demirer, R., Balcilar, M., & Gupta, R. (2023). On the pricing effects of bitcoin mining in the fossil fuel market: The case of coal. Resources Policy, 85, 103539. https://doi.org/10.1016/j.resourpol.2023.103539
   Web of Science ®Google Scholar
• Su, C. W., Pang, L. D., Tao, R., Shao, X., & Umar, M. (2022). Renewable energy and technological innovation: Which one is the winner in promoting net-zero emissions? Technological Forecasting and Social Change, 182, 121798. https://doi.org/10.1016/j.techfore.2022.121798
   Web of Science ®Google Scholar
• Taghizadeh-Hesary, F., Yoshino, N., & Phoumin, H. (2021). Analyzing the characteristics of green bond markets to facilitate green finance in the post-COVID-19 world. Sustainability (Basel), 13(10), 5719. https://doi.org/10.3390/su13105719
   Web of Science ®Google Scholar
• Tan, X., Geng, Y., Vivian, A., & Wang, X. (2021). Measuring risk spillovers between oil and clean energy stocks: Evidence from a systematic framework. Resources Policy, 74, 102406. https://doi.org/10.1016/j.resourpol.2021.102406
   Web of Science ®Google Scholar
• Tan, X., Sirichand, K., Vivian, A., & Wang, X. (2022). Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals. International Journal of Forecasting, 38(3), 944–969. https://doi.org/10.1016/j.ijforecast.2021.07.005
   Web of Science ®Google Scholar
• Tiwari, A. K., Abakah, E. J. A., Shao, X., Le, T.-L., & Gyamfi, M. N. (2023). Financial technology stocks, green financial assets, and energy markets: A quantile causality and dependence analysis. Energy Economics, 118, 106498. https://doi.org/10.1016/j.eneco.2022.106498
   Web of Science ®Google Scholar
• Umar, M., Farid, S., & Naeem, M. A. (2022). Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial, oil and pandemic crisis. Energy, 240, 122702. https://doi.org/10.1016/j.energy.2021.122702
   Web of Science ®Google Scholar
• Wang, J., Ma, F., Bouri, E., & Zhong, J. (2022). Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions. Energy Economics, 108, 105904. https://doi.org/10.1016/j.eneco.2022.105904
   Web of Science ®Google Scholar
• Yadav, M. P., Pandey, A., Taghizadeh-Hesary, F., Arya, V., & Mishra, N. (2023). Volatility spillover of green bond with renewable energy and crypto market. Renewable Energy, 212, 928–939. https://doi.org/10.1016/j.renene.2023.05.056
   Web of Science ®Google Scholar
• Yousaf, I., Nekhili, R., & Umar, M. (2022). Extreme connectedness between renewable energy tokens and fossil fuel markets. Energy Economics, 114, 106305. https://doi.org/10.1016/j.eneco.2022.106305
   Web of Science ®Google Scholar
• Zhang, J., Chen, X., Wei, Y., & Bai, L. (2023). Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis. International Review of Financial Analysis, 88, 102659. https://doi.org/10.1016/j.irfa.2023.102659
   Web of Science ®Google Scholar