Dynamic co-movement analysis among oil prices, green bonds, and CO2 emissions, 2014-2022

dc.contributorBotero Botero, Sergio
dc.contributorGonzález Ruiz, Juan David
dc.contributorModelamiento y Análisis Energía Ambiente Economía
dc.contributorMarín-Rodríguez, Nini Johana [0000-0003-4318-7947]
dc.contributorGonzález Ruiz, Juan David [0000-0003-4425-7687]
dc.contributorMarín-Rodríguez, Nini Johana [0001337439]
dc.contributorMarín-Rodríguez, Nini Johana [57195913643]
dc.contributorMarín-Rodríguez, Nini Johana [ J-4437-2015]
dc.contributorMarín-Rodríguez, Nini Johana [https://scholar.google.com/citations?user=QnylAiQAAAAJ&hl=es]
dc.creatorMarín-Rodríguez, Nini Johana
dc.date.accessioned2023-05-30T12:47:08Z
dc.date.available2023-05-30T12:47:08Z
dc.date.created2023-05-30T12:47:08Z
dc.date.issued2023-05-29
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/83906
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractThis research addresses the problem of the coverage gap in the extant literature to know how oil prices, green bonds, and CO2 emissions are related to each other. Additionally, to research the short and long-term relations using a machine learning model for measuring co-movements among these important variables in the global energy transition context. Therefore, this study’s primary objective is to analyze the results of the short- and long-term co-movements and the implications for researchers, investors, and policy-makers. To validate the analysis, we use daily data from oil prices, green bonds, and CO2 emissions from 2014 to 2022. In addition, a scientometric analysis of the principal methodologies for measuring the co-movements among financial markets, using techniques such as the analysis of (i) sources, (ii) authors, (iii) documents, and (iv) cluster analysis. In this way, this research applies methodologies like Granger Causality Test, Dynamic Conditional Correlation (DCC-Garch), Wavelet power spectrum (WPS), and wavelet coherence analyses (WCA). Additionally, this study employs a machine learning model for measuring the relationships among the selected variables. Specifically, the Fuzzy Logistic Autoencoder (FLAE) was implemented. Furthermore, the results of the machine learning model were validated and compared with the estimated models. Finally, this study represents a breakthrough in explaining the relationship among these variables.
dc.description.abstractEsta investigación aborda el vacío en la literatura existente sobre cómo se relacionan entre sí los precios del petróleo, los bonos verdes y las emisiones de CO2. Además, se investigan las relaciones a corto y largo plazo de los co-movimientos entre estas importantes variables en el contexto de la transición energética mundial, utilizando un modelo de aprendizaje automático. Por lo tanto, el objetivo principal de este estudio es analizar los resultados de los co-movimientos a corto y largo plazo y las implicaciones para investigadores, inversores y responsables de política. Para validar el análisis, utilizamos datos diarios de los precios del petróleo, los bonos verdes y las emisiones de CO2 desde 2014 hasta 2022. Además, se realiza un análisis cienciométrico de las principales metodologías para medir los co-movimientos entre los mercados financieros, utilizando técnicas como el análisis de (i) fuentes, (ii) autores, (iii) documentos, y (iv) análisis de clusters. De este modo, esta investigación aplica metodologías como la prueba de causalidad de Granger, la correlación condicional dinámica (Dynamic Conditional Correlation, DCC-Garch), el espectro de potencia wavelet (Wavelet Power Spectrum, WPS) y el análisis de coherencia wavelet (Wavelet Coherence Analyses, WCA). Además, este estudio emplea un modelo de aprendizaje automático para medir las relaciones entre las variables seleccionadas. En concreto, se implementó el autoencoder logístico difuso (Fuzzy Logistic Autoencoder, FLAE). Además, los resultados del modelo de aprendizaje automático se validaron y compararon con los modelos estimados. Por último, este estudio representa un avance en la explicación de la relación entre estas variables. (Texto tomado de la fuente)
dc.languageeng
dc.publisherUniversidad Nacional de Colombia
dc.publisherMedellín - Minas - Doctorado en Ingeniería - Industria y Organizaciones
dc.publisherFacultad de Minas
dc.publisherMedellín, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
dc.relationRedCol
dc.relationLaReferencia
dc.relationAbdelmaksoud, A. M., Balomenos, G. P., & Becker, T. C. (2022). Fuzzy-Logistic Models for Incorporating Epistemic Uncertainty in Bridge Management Decisions. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8(3), 04022025.
dc.relationAddison, P. S. (2017). The Illustrated Wavelet Transform Handbook. CRC Press. https://doi.org/10.1201/9781315372556
dc.relationAdom, P. K., Kwakwa, P. A., & Amankvvaa, A. (2018). The long-run effects of economic, demographic, and political indices on actual and potential CO2 emissions. Journal of Environmental Management, 218, 516–526. https://doi.org/10.1016/j.jenvman.2018.04.090
dc.relationAgboola, M. O., Bekun, F. V., & Balsalobre-Lorente, D. (2021). Implications of Social Isolation in Combating COVID-19 Outbreak in Kingdom of Saudi Arabia: Its Consequences on the Carbon Emissions Reduction. Sustainability, 13(16), 9476. https://doi.org/10.3390/su13169476
dc.relationAguiar-Conraria, L., & Soares, M. J. (2011). Oil and the macroeconomy: using wavelets to analyze old issues. Empirical Economics, 40(3), 645–655. https://doi.org/10.1007/s00181-010-0371-x
dc.relationAhmed, S., Chakrabortty, R. K., Essam, D. L., & Ding, W. (2022). Poly-linear regression with augmented long short term memory neural network: Predicting time series data. Information Sciences, 606, 573–600. https://doi.org/10.1016/j.ins.2022.05.078
dc.relationAhmed, W. M. A. (2022). On the higher-order moment interdependence of stock and commodity markets: A wavelet coherence analysis. The Quarterly Review of Economics and Finance, 83, 135–151. https://doi.org/10.1016/j.qref.2021.12.003
dc.relationAkca, H. (2021). Environmental Kuznets Curve and financial development in Turkey: evidence from augmented ARDL approach. Environmental Science and Pollution Research, 28(48), 69149–69159. https://doi.org/10.1007/s11356-021-15417-w
dc.relationAkkoc, U., & Civcir, I. (2019). Dynamic linkages between strategic commodities and stock market in Turkey: Evidence from SVAR-DCC-GARCH model. Resources Policy, 62, 231–239. https://doi.org/10.1016/j.resourpol.2019.03.017
dc.relationAkram, V., & Haider, S. (2022). A Dynamic Nexus Between COVID-19 Sentiment, Clean Energy Stocks, Technology Stocks, and Oil Prices: Global Evidence. Energy RESEARCH LETTERS, 3(3). https://doi.org/10.46557/001c.32625
dc.relational Mamun, M., Boubaker, S., & Nguyen, D. K. (2022). Green finance and decarbonization: Evidence from around the world. Finance Research Letters, 46, 102807. https://doi.org/10.1016/j.frl.2022.102807
dc.relationAlbulescu, C. T., Demirer, R., Raheem, I. D., & Tiwari, A. K. (2019). Does the U.S. economic policy uncertainty connect financial markets? Evidence from oil and commodity currencies. Energy Economics, 83, 375–388. https://doi.org/https://doi.org/10.1016/j.eneco.2019.07.024
dc.relationAlhodiry, A., Rjoub, H., & Samour, A. (2021). Impact of oil prices, the U.S interest rates on Turkey’s real estate market. New evidence from combined co-integration and bootstrap ARDL tests. Plos One, 16(1), e0242672. https://doi.org/10.1371/journal.pone.0242672
dc.relationAli, M., Tursoy, T., Samour, A., Moyo, D., & Konneh, A. (2022). Testing the impact of the gold price, oil price, and renewable energy on carbon emissions in South Africa: Novel evidence from bootstrap ARDL and NARDL approaches. Resources Policy, 79, 102984. https://doi.org/10.1016/j.resourpol.2022.102984
dc.relationAli, S. R. M., Mensi, W., Anik, K. I., Rahman, M., & Kang, S. H. (2022). The impacts of COVID-19 crisis on spillovers between the oil and stock markets: Evidence from the largest oil importers and exporters. Economic Analysis and Policy, 73, 345–372. https://doi.org/10.1016/j.eap.2021.11.009
dc.relationAlkathery, M. A., & Chaudhuri, K. (2021). Co-movement between oil price, CO<inf>2</inf> emission,renewable energy and energy equities: Evidence from GCC countries. Journal of Environmental Management, 297. https://doi.org/10.1016/j.jenvman.2021.113350
dc.relationAllen, R. G. D. (1950). The Substitution Effect in Value Theory. The Economic Journal, 60(240), 675. https://doi.org/10.2307/2226707
dc.relationAloui, R., ben Aïssa, M. S., & Nguyen, D. K. (2013). Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach. Journal of International Money and Finance, 32(1), 719–738. https://doi.org/10.1016/j.jimonfin.2012.06.006
dc.relationAloui, R., Hammoudeh, S., & Nguyen, D. K. (2013). A time-varying copula approach to oil and stock market dependence: The case of transition economies. Energy Economics, 39, 208–221. https://doi.org/10.1016/j.eneco.2013.04.012
dc.relationAlshdadi, A. A., Hayat, M. K., Daud, A., Banjar, A., & Dawood, H. (2022). Measuring the impact of COVID-19 surveillance variables over the international oil market. International Journal of Advanced and Applied Sciences, 9(1), 27–33. https://doi.org/10.21833/IJAAS.2022.01.004
dc.relationAlshehry, A. S., & Belloumi, M. (2017). Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia. Renewable and Sustainable Energy Reviews, 75, 1339–1347. https://doi.org/10.1016/j.rser.2016.11.122
dc.relationAmano, R. A., & van Norden, S. (1998a). Exchange rates and oil prices. Review of International Economics, 6(4), 683–694. https://doi.org/10.1111/1467-9396.00136
dc.relationAmano, R. A., & van Norden, S. (1998b). Oil prices and the rise and fall of the US real exchange rate. Journal of International Money and Finance, 17(2), 299–316. https://doi.org/10.1016/S0261-5606(98)00004-7
dc.relationAndrews, D. W. K. (2003). Tests for parameter instability and structural change with unknown change point: A corrigendum. Econometrica, 71(1), 395–397. https://doi.org/10.1111/1468-0262.00405
dc.relationAntonakakis, N., Chatziantoniou, I., & Filis, G. (2013). Dynamic co-movements of stock market returns, implied volatility and policy uncertainty. Economics Letters, 120(1), 87–92. https://doi.org/10.1016/j.econlet.2013.04.004
dc.relationAntonakakis, N., Chatziantoniou, I., & Filis, G. (2017). Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest. International Review of Financial Analysis, 50, 1–26. https://doi.org/10.1016/j.irfa.2017.01.004
dc.relationApergis, N., & Miller, S. M. (2009). Do structural oil-market shocks affect stock prices? Energy Economics, 31(4), 569–575. https://doi.org/10.1016/j.eneco.2009.03.001
dc.relationApergis, N., & Payne, J. E. (2015). Renewable energy, output, carbon dioxide emissions, and oil prices: Evidence from South America. Energy Sources, Part B: Economics, Planning and Policy, 10(3), 281–287. https://doi.org/10.1080/15567249.2013.853713
dc.relationAria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
dc.relationArouri, M. E. H., Jouini, J., & Nguyen, D. K. (2012). On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness. Energy Economics, 34(2), 611–617. https://doi.org/10.1016/j.eneco.2011.08.009
dc.relationAzhgaliyeva, D., Kapoor, A., & Liu, Y. (2020). Green bonds for financing renewable energy and energy efficiency in South-East Asia: a review of policies. Journal of Sustainable Finance and Investment, 10(2), 113–140. https://doi.org/10.1080/20430795.2019.1704160
dc.relationAzhgaliyeva, 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
dc.relationAzhgaliyeva, D., Mishra, R., & Kapsalyamova, Z. (2021). Oil Price Shocks and Green Bonds: A Longitudinal Multilevel Model (ADBI Working Paper 1278). Asian Development Bank. https://www.adb.org/publications/oil-price-shocks-green-bonds-longitudinal-multilevel-model
dc.relationBai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659
dc.relationBalaguer, J., & Cantavella, M. (2016). Estimating the environmental Kuznets curve for Spain by considering fuel oil prices (1874–2011). Ecological Indicators, 60, 853–859. https://doi.org/10.1016/j.ecolind.2015.08.006
dc.relationBali, T. G., & Engle, R. F. (2010). The intertemporal capital asset pricing model with dynamic conditional correlations. Journal of Monetary Economics, 57(4), 377–390. https://doi.org/10.1016/j.jmoneco.2010.03.002
dc.relationBalsalobre-Lorente, D., Driha, O. M., Bekun Festus Victor and Sinha, A., & Adedoyin, F. F. (2020). Consequences of COVID-19 on the social isolation of the Chinese economy: accounting for the role of reduction in carbon emissions. Air Quality Atmosphere And Health, 13(12), 1439–1451. https://doi.org/10.1007/s11869-020-00898-4
dc.relationBarsky, R. B., & Kilian, L. (2004). Oil and the Macroeconomy Since the 1970s. Journal of Economic Perspectives, 18(4), 115–134. https://doi.org/10.1257/0895330042632708
dc.relationBasher, S. A., Haug, A. A., & Sadorsky, P. (2012). Oil prices, exchange rates and emerging stock markets. Energy Economics, 34(1), 227–240.
dc.relationBasher, S. A., & Sadorsky, P. (2006). Oil price risk and emerging stock markets. Global Finance Journal, 17(2), 224–251. https://doi.org/10.1016/j.gfj.2006.04.001
dc.relationBasher, S. A., & Sadorsky, P. (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235–247. https://doi.org/10.1016/j.eneco.2015.11.022
dc.relationBashir, M. F. (2022). Oil price shocks, stock market returns, and volatility spillovers: a bibliometric analysis and its implications. Environmental Science and Pollution Research, 29(16), 22809–22828. https://doi.org/10.1007/s11356-021-18314-4
dc.relationBashir, M. F., MA, B., Shahbaz, M., & Jiao, Z. (2020). The nexus between environmental tax and carbon emissions with the roles of environmental technology and financial development. Plos One, 15(11), e0242412. https://doi.org/10.1371/journal.pone.0242412
dc.relationBashir, M. F., MA, B., Shahzad, L., Liu, B., & Ruan, Q. (2021). China’s quest for economic dominance and energy consumption: Can Asian economies provide natural resources for the success of One Belt One Road? Managerial and Decision Economics, 42(3), 570–587. https://doi.org/10.1002/mde.3255
dc.relationBassey, E. (2015). Oil price: Effect on carbon emission. Carbon Management Technology Conference 2015: Sustainable and Economical CCUS Options, CMTC 2015, 1, 37–51.
dc.relationBaur, D. G. (2012). Financial contagion and the real economy. Journal of Banking & Finance, 36(10), 2680–2692. https://doi.org/10.1016/j.jbankfin.2011.05.019
dc.relationBayar, Y., Sasmaz, M. U., & Ozkaya, M. H. (2021). Impact of Trade and Financial Globalization on Renewable Energy in EU Transition Economies: A Bootstrap Panel Granger Causality Test. Energies, 14(1). https://doi.org/10.3390/en14010019
dc.relationBehmiri, N. B., & Pires Manso, J. R. (2012). Crude oil conservation policy hypothesis in OECD (organisation for economic cooperation and development) countries: A multivariate panel Granger causality test. Energy, 43(1), 253–260. https://doi.org/10.1016/j.energy.2012.04.032
dc.relationBeirne, J., & Gieck, J. (2014). Interdependence and contagion in global asset markets. Review of International Economics, 22(4), 639–659.
dc.relationBelhassine, O. (2020). Volatility spillovers and hedging effectiveness between the oil market and Eurozone sectors: A tale of two crises. Research in International Business and Finance, 53. https://doi.org/10.1016/j.ribaf.2020.101195
dc.relationBengio, Y. (2012). Deep Learning of Representations for Unsupervised and Transfer Learning. In I. Guyon, G. Dror, V. Lemaire, G. Taylor, & D. Silver (Eds.), Proceedings of ICML Workshop on Unsupervised and Transfer Learning (Vol. 27, pp. 17–36). PMLR. https://proceedings.mlr.press/v27/bengio12a.html
dc.relationBhavsar, H., Jivani, A., Amesara, S., Shah, S., Gindani, P., & Patel, S. (2023). Stock Price Prediction Using Sentiment Analysis on News Headlines (pp. 25–34). https://doi.org/10.1007/978-981-19-3571-8_4
dc.relationBloomberg, & MSCI. (2021). Bloomberg MSCI Green Bond Indices. Bringing clarity to the green bond market through benchmark indices. In Manual. https://www.msci.com/documents/1296102/26180598/BBG+MSCI+Green+Bond+Indices+Primer.pdf
dc.relationBloomfield, P. (2013). Fourier analysis of time series: an introduction (Second Edition). John Wiley & Sons.
dc.relationBodart, V., & Candelon, B. (2009). Evidence of interdependence and contagion using a frequency domain framework. Emerging Markets Review, 10(2), 140–150. https://doi.org/10.1016/j.ememar.2008.11.003
dc.relationBoersen, A., & Scholtens, B. (2014). The relationship between European electricity markets and emission allowance futures prices in phase II of the EU (European Union) emission trading scheme. Energy, 74, 585–594. https://doi.org/10.1016/j.energy.2014.07.024
dc.relationBoldanov, R., Degiannakis, S., & Filis, G. (2016). Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries. International Review of Financial Analysis, 48, 209–220. https://doi.org/10.1016/j.irfa.2016.10.002
dc.relationBollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
dc.relationBoufateh, T. (2019). The environmental Kuznets curve by considering asymmetric oil price shocks: evidence from the top two. Environmental Science and Pollution Research, 26(1), 706–720. https://doi.org/10.1007/s11356-018-3641-3
dc.relationBouoiyour, 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
dc.relationBouri, E., Chen, Q., Lien, D., & Lv, X. (2017). Causality between oil prices and the stock market in China: The relevance of the reformed oil product pricing mechanism. International Review of Economics and Finance, 48, 34–48. https://doi.org/10.1016/j.iref.2016.11.004
dc.relationBouri, E., Shahzad, S. J. H., Roubaud, D., Kristoufek, L., & Lucey, B. (2020). Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. The Quarterly Review of Economics and Finance, 77, 156–164. https://doi.org/https://doi.org/10.1016/j.qref.2020.03.004
dc.relationBradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.
dc.relationBroadstock, 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/https://doi.org/10.1016/j.frl.2019.02.006
dc.relationBurandt, T. (2021). Decarbonizing the global energy system : modelling global and regional transformation pathways with multi-sector energy system models [Technische Universität Berlin]. https://doi.org/10.14279/depositonce-12079
dc.relationCaporin, M., & McAleer, M. (2013). Ten things you should know about the dynamic conditional correlation representation. Econometrics, 1(1), 115–126. https://doi.org/10.3390/econometrics1010115
dc.relationCappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572. https://doi.org/10.1093/jjfinec/nbl005
dc.relationCavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
dc.relationÇelik, T. Ö., Jamneshan, A., Montúfar, G., Sturmfels, B., & Venturello, L. (2021). Wasserstein distance to independence models. Journal of Symbolic Computation, 104, 855–873. https://doi.org/10.1016/j.jsc.2020.10.005
dc.relationChang, K., Ye, Z., & Wang, W. (2019). Volatility spillover effect and dynamic correlation between regional emissions allowances and fossil energy markets: New evidence from China’s emissions trading scheme pilots. Energy, 185, 1314–1324. https://doi.org/https://doi.org/10.1016/j.energy.2019.07.132
dc.relationChansanam, W., & Li, C. (2022). Scientometrics of Poverty Research for Sustainability Development: Trend Analysis of the 1964–2022 Data through Scopus. Sustainability, 14(9), 5339. https://doi.org/10.3390/su14095339
dc.relationCharte, D., Charte, F., & Herrera, F. (2022). Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9549–9560. https://doi.org/10.1109/TPAMI.2021.3127698
dc.relationChen, N.-F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 383–403.
dc.relationChen, Q., & Taylor, D. (2020). Economic development and pollution emissions in Singapore: Evidence in support of the Environmental Kuznets Curve hypothesis and its implications for regional sustainability. Journal of Cleaner Production, 243, 118637. https://doi.org/10.1016/j.jclepro.2019.118637
dc.relationChen, S.-S. (2010). Do higher oil prices push the stock market into bear territory? Energy Economics, 32(2), 490–495. https://doi.org/10.1016/j.eneco.2009.08.018
dc.relationChen, X., Lun, Y., Yan, J., Hao, T., & Weng, H. (2019). Discovering thematic change and evolution of utilizing social media for healthcare research. BMC Medical Informatics and Decision Making, 19(S2), 50. https://doi.org/10.1186/s12911-019-0757-4
dc.relationChen, Y., Qu, F., Li, W., & Chen, M. (2019). Volatility spillover and dynamic correlation between the carbon market and energy markets. Journal of Business Economics and Management, 20(5), 979–999. https://doi.org/10.3846/jbem.2019.10762
dc.relationChen, Y.-C., & Rogoff, K. (2003). Commodity currencies. Journal of International Economics, 60(1), 133–160. https://doi.org/10.1016/S0022-1996(02)00072-7
dc.relationCheng, H., Damerow, L., Sun, Y., & Blanke, M. (2017). Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks. Journal of Imaging, 3(1), 6. https://doi.org/10.3390/jimaging3010006
dc.relationCherubini, F. (2010). The biorefinery concept: Using biomass instead of oil for producing energy and chemicals. Energy Conversion and Management, 51(7), 1412–1421. https://doi.org/10.1016/j.enconman.2010.01.015
dc.relationChevallier, J. (2012). Time-varying correlations in oil, gas and CO2 prices: an application using BEKK, CCC and DCC-MGARCH models. Applied Economics, 44(32), 4257–4274.
dc.relationChiang, T. C., Jeon, B. N., & Li, H. (2007). Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money and Finance, 26(7), 1206–1228.
dc.relationChoi, D., Gao, Z., & Jiang, W. (2020). Attention to global warming. Review of Financial Studies, 33(3), 1112–1145. https://doi.org/10.1093/rfs/hhz086
dc.relationCiner, C. (2001). Energy Shocks and Financial Markets: Nonlinear Linkages. Studies in Nonlinear Dynamics & Econometrics, 5(3). https://doi.org/10.2202/1558-3708.1079
dc.relationCivcir, İ., & Akkoç, U. (2021). Dynamic volatility linkages and hedging between commodities and sectoral stock returns in Turkey: Evidence from SVAR-cDCC-GARCH model. International Journal of Finance and Economics, 26(2), 1978–1992. https://doi.org/10.1002/ijfe.1889
dc.relationColacito, R., Engle, R. F., & Ghysels, E. (2011). A component model for dynamic correlations. Journal of Econometrics, 164(1), 45–59. https://doi.org/10.1016/j.jeconom.2011.02.013
dc.relationCong, R.-G., Wei, Y.-M., Jiao, J.-L., & Fan, Y. (2008). Relationships between oil price shocks and stock market: An empirical analysis from China. Energy Policy, 36(9), 3544–3553. https://doi.org/10.1016/j.enpol.2008.06.006
dc.relationCramer, E., Gorjao, L. R., Mitsos, A., Schafer, B., Witthaut, D., & Dahmen, M. (2022). Validation Methods for Energy Time Series Scenarios From Deep Generative Models. IEEE Access, 10, 8194–8207. https://doi.org/10.1109/ACCESS.2022.3141875
dc.relationCreti, A., Joëts, M., & Mignon, V. (2013). On the links between stock and commodity markets’ volatility. Energy Economics, 37, 16–28. https://doi.org/10.1016/j.eneco.2013.01.005
dc.relationDarby, M. R. (1982). The price of oil and world inflation and recession. American Economic Review, 72(4), 738–751.
dc.relationde Nard, G., Engle, R. F., Ledoit, O., & Wolf, M. (2022). Large dynamic covariance matrices: Enhancements based on intraday data. Journal of Banking and Finance, 138. https://doi.org/10.1016/j.jbankfin.2022.106426
dc.relationde Souza, E. S., Freire, F. de S., & Pires, J. (2018). Determinants of CO2 emissions in the MERCOSUR: the role of economic growth, and renewable and non-renewable energy. Environmental Science and Pollution Research, 25(21, SI), 20769–20781. https://doi.org/10.1007/s11356-018-2231-8
dc.relationDemir, S., Mincev, K., Kok, K., & Paterakis, N. G. (2021). Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting. Applied Energy, 304, 117695. https://doi.org/10.1016/j.apenergy.2021.117695
dc.relationDewandaru, G., Rizvi, S. A. R., Masih, R., Masih, M., & Alhabshi, S. O. (2014). Stock market co-movements: Islamic versus conventional equity indices with multi-timescales analysis. Economic Systems, 38(4), 553–571. https://doi.org/10.1016/j.ecosys.2014.05.003
dc.relationDibal, P. Y., Onwuka, E. N., Agajo, J., & Alenoghena, C. O. (2018). Application of wavelet transform in spectrum sensing for cognitive radio: A survey. Physical Communication, 28, 45–57. https://doi.org/10.1016/j.phycom.2018.03.004
dc.relationDickey, 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(366a), 427–431. https://doi.org/doi.org/10.1080/01621459.1979.10482531
dc.relationDiebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
dc.relationDiebold, 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
dc.relationDisli, M., Nagayev, R., Salim, K., Rizkiah, S. K., & Aysan, A. F. (2021). In search of safe haven assets during COVID-19 pandemic: An empirical analysis of different investor types. Research in International Business and Finance, 58, 101461. https://doi.org/10.1016/j.ribaf.2021.101461
dc.relationDong, F., Gao, Y., Li, Y., Zhu, J., Hu, M., & Zhang, X. (2022). Exploring volatility of carbon price in European Union due to COVID-19 pandemic. Environmental Science and Pollution Research, 29(6), 8269–8280. https://doi.org/10.1007/s11356-021-16052-1
dc.relationDutta, A. (2018). Implied volatility linkages between the U.S. and emerging equity markets: A note. Global Finance Journal, 35, 138–146. https://doi.org/10.1016/j.gfj.2017.09.002
dc.relationDutta, A., Bouri, E., & Noor, M. H. (2018). Return and volatility linkages between CO2 emission and clean energy stock prices. Energy, 164, 803–810. https://doi.org/10.1016/j.energy.2018.09.055
dc.relationDutta, A., Bouri, E., & Noor, M. H. (2021). Climate bond, stock, gold, and oil markets: Dynamic correlations and hedging analyses during the COVID-19 outbreak. Resources Policy, 74, 102265. https://doi.org/10.1016/j.resourpol.2021.102265
dc.relationElder, J., & Serletis, A. (2010). Oil Price Uncertainty. Journal of Money, Credit and Banking, 42(6), 1137–1159. https://doi.org/10.1111/j.1538-4616.2010.00323.x
dc.relationElie, B., Naji, J., Dutta, A., & Uddin, G. S. (2019). Gold and crude oil as safe-haven assets for clean energy stock indices: Blended copulas approach. Energy, 178, 544–553. https://doi.org/10.1016/j.energy.2019.04.155
dc.relationEngle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350.
dc.relationEngle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251. https://doi.org/10.2307/1913236
dc.relationEngle, R. F., Ledoit, O., & Wolf, M. (2019). Large Dynamic Covariance Matrices. Journal of Business and Economic Statistics, 37(2), 363–375. https://doi.org/10.1080/07350015.2017.1345683
dc.relationEngle, R., & Kroner, K. (1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11, 122–150.
dc.relationErdogan, S., Okumus, I., & Guzel, A. E. (2020). Revisiting the Environmental Kuznets Curve hypothesis in OECD countries: the role of renewable, non-renewable energy, and oil prices. Environmental Science and Pollution Research, 27(19), 23655–23663. https://doi.org/10.1007/s11356-020-08520-x
dc.relationFatica, S., & Panzica, R. (2021). Green bonds as a tool against climate change? Business Strategy and the Environment, 30(5), 2688–2701. https://doi.org/10.1002/bse.2771
dc.relationFawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019). Deep learning for time series classification: a review. Data Mining and Knowledge Discovery, 33(4), 917–963. https://doi.org/10.1007/s10618-019-00619-1
dc.relationFeng, Y., & Cui, Y. (2022). Dual and single hedging strategy: a novel comparison from the direct and cross hedging perspective. China Finance Review International, 12(1), 161–179. https://doi.org/10.1108/CFRI-05-2020-0053
dc.relationFilis, G., Degiannakis, S., & Floros, C. (2011). Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20(3), 152–164. https://doi.org/10.1016/j.irfa.2011.02.014
dc.relationForbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. Journal of Finance, 57(5), 2223–2261. https://doi.org/10.1111/0022-1082.00494
dc.relationForbes, K., & Rigobon, R. (2001). Measuring contagion: conceptual and empirical issues. In International financial contagion (pp. 43–66). Springer.
dc.relationFtiti, Z., Guesmi, K., & Abid, I. (2016). Oil price and stock market co-movement: What can we learn from time-scale approaches? International Review of Financial Analysis, 46, 266–280. https://doi.org/10.1016/j.irfa.2015.08.011
dc.relationFtiti, Z., Guesmi, K., Teulon, F., & Chouachi, S. (2016). Relationship between crude oil prices and economic growth in selected OPEC countries. Journal of Applied Business Research, 32(1), 11–22. https://doi.org/10.19030/jabr.v32i1.9483
dc.relationGajurel, D., & Chawla, A. (2022). The oil price crisis and contagion effects on the Canadian economy. Applied Economics, 54(13), 1527–1543. https://doi.org/10.1080/00036846.2021.1980196
dc.relationGarfield, E. (1970). Citation Indexing for Studying Science. Nature, 227(5259), 669–671. https://doi.org/10.1038/227669a0
dc.relationGhorbali, B., Naoui, K., & Derbali, A. (2022). Co-movement Among COVID-19 Pandemic, Crude Oil, Stock Market of US, and Bitcoin: Empirical Evidence from WCA. In Accounting, Finance, Sustainability, Governance and Fraud. https://doi.org/10.1007/978-981-19-1036-4_3
dc.relationGiuliodori, A., Berrone, P., & Ricart, J. E. (2022). Where smart meets sustainability: The role of Smart Governance in achieving the Sustainable Development Goals in cities. BRQ Business Research Quarterly, 234094442210912. https://doi.org/10.1177/23409444221091281
dc.relationGolub, S. S. (1983). Oil Prices and Exchange Rates. The Economic Journal, 93(371), 576. https://doi.org/10.2307/2232396
dc.relationGonzález-Ruiz, J. D., Mejía-Escobar, J. C., Rojo-Suárez, J., & Alonso-Conde, A.-B. (2023). Green Bonds for Renewable Energy in Latin America and the Caribbean. The Energy Journal, 44(01). https://doi.org/10.5547/01956574.44.4.jgon
dc.relationGonzalez-Ruiz, J. D., Peña, A., Duque, E. A., Patiño, A., Chiclana, F., & Góngora, M. (2019). Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects. Applied Soft Computing, 85, 105818. https://doi.org/10.1016/j.asoc.2019.105818
dc.relationGonzález-Ruiz, J., Mejía-Escobar, J., Rojo-Suárez, J., & Alonso-Conde, A. (2023). Green Bonds for Renewable Energy in Latin America and the Caribbean. The Energy Journal, 44(5), 25–45. https://doi.org/10.5547/01956574.44.4.jgon
dc.relationGranger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. In E. Ghysels, N. R. Swanson, & M. W. Watson (Eds.), Essays in Econometrics (Vol. 2, pp. 31–47). Cambridge University Press. https://doi.org/10.1017/CBO9780511753978.002
dc.relationGrinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), 561–566. https://doi.org/10.5194/npg-11-561-2004
dc.relationGustafsson, R., Dutta, A., & Bouri, E. (2022). Are energy metals hedges or safe havens for clean energy stock returns? Energy, 244, 122708. https://doi.org/10.1016/j.energy.2021.122708
dc.relationHabib, Y., Xia, E., Fareed, Z., & Hashmi, S. H. (2021). Time–frequency co-movement between COVID-19, crude oil prices, and atmospheric CO2 emissions: Fresh global insights from partial and multiple coherence approach. Environment, Development and Sustainability, 23(6), 9397–9417. https://doi.org/10.1007/s10668-020-01031-2
dc.relationHamilton, J. D. (1983). Oil and the macroeconomy since world war II. Journal of Political Economy, 91(2), 228–248. https://doi.org/10.1086/261140
dc.relationHamilton, J. D. (2003). What is an oil shock? Journal of Econometrics, 113(2), 363–398. https://doi.org/10.1016/S0304-4076(02)00207-5
dc.relationHammoudeh, S., Ajmi, A. N., & Mokni, K. (2020). Relationship between green bonds and financial and environmental variables: A novel time-varying causality. Energy Economics, 92, 104941. https://doi.org/https://doi.org/10.1016/j.eneco.2020.104941
dc.relationHammoudeh, S., Dibooglu, S., & Aleisa, E. (2004). Relationships among U.S. oil prices and oil industry equity indices. International Review of Economics & Finance, 13(4), 427–453. https://doi.org/10.1016/S1059-0560(03)00011-X
dc.relationHansun, S., Putri, F. P., M. Khaliq, A. Q., & Hugeng, H. (2022). On searching the best mode for forex forecasting: bidirectional long short-term memory default mode is not enough. IAES International Journal of Artificial Intelligence (IJ-AI), 11(4), 1596. https://doi.org/10.11591/ijai.v11.i4.pp1596-1606
dc.relationHenriques, 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
dc.relationHernán, M. A., Hsu, J., & Healy, B. (2019). A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks. Chance, 32(1), 42–49. https://doi.org/10.1080/09332480.2019.1579578
dc.relationHuang, R. D., Masulis, R. W., & Stoll, H. R. (1996). Energy shocks and financial markets. Journal of Futures Markets, 16(1), 1–27. https://doi.org/10.1002/(sici)1096-9934(199602)16:1<1::aid-fut1>3.3.co;2-g
dc.relationHudgins, L., Friehe, C. A., & Mayer, M. E. (1993). Wavelet transforms and atmopsheric turbulence. Physical Review Letters, 71(20), 3279–3282. https://doi.org/10.1103/PhysRevLett.71.3279
dc.relationHung, N. T. (2021). Nexus between green bonds, financial, and environmental indicators. Economics and Business Letters, 10(3), 191–199. https://doi.org/10.17811/ebl.10.3.2021.191-199
dc.relationHusaini, D. H., Lean, H. H., & Ab-Rahim, R. (2021). The relationship between energy subsidies, oil prices, and CO2 emissions in selected Asian countries: a panel threshold analysis. Australasian Journal of Environmental Management, 28(4), 339–354. https://doi.org/10.1080/14486563.2021.1961620
dc.relationJammazi, R. (2012). Cross dynamics of oil-stock interactions: A redundant wavelet analysis. Energy, 44(1), 750–777. https://doi.org/10.1016/j.energy.2012.05.017
dc.relationJammazi, R., & Reboredo, J. C. (2016). Dependence and risk management in oil and stock markets. A wavelet-copula analysis. Energy, 107, 866–888. https://doi.org/10.1016/j.energy.2016.02.093
dc.relationJiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised Machine Learning: A Brief Primer. Behavior Therapy, 51(5), 675–687. https://doi.org/10.1016/j.beth.2020.05.002
dc.relationJin, J., Han, L., Wu, L., & Zeng, H. (2020). The hedging effect of green bonds on carbon market risk. International Review of Financial Analysis, 71. https://doi.org/10.1016/j.irfa.2020.101509
dc.relationJones, C. M., & Kaul, G. (1996). Oil and the stock markets. Journal of Finance, 51(2), 463–491. https://doi.org/10.1111/j.1540-6261.1996.tb02691.x
dc.relationKang, W., & Ratti, R. A. (2013). Structural oil price shocks and policy uncertainty. Economic Modelling, 35, 314–319. https://doi.org/10.1016/j.econmod.2013.07.025
dc.relationKassouri, Y., Bilgili, F., & Kuşkaya, S. (2022). A wavelet-based model of world oil shocks interaction with CO2 emissions in the US. Environmental Science & Policy, 127, 280–292. https://doi.org/10.1016/j.envsci.2021.10.020
dc.relationKassouri, Y., Kacou, K. Y. T., & Alola, A. A. (2021). Are oil-clean energy and high technology stock prices in the same straits? Bubbles speculation and time-varying perspectives. Energy, 232, 121021. https://doi.org/10.1016/j.energy.2021.121021
dc.relationKhan, I., Rehman, F. U., Pyplacz, P., Khan, M. A., Wisniewska, A., & Liczmanska-Kopcewicz, K. (2021). A Dynamic Linkage between Financial Development, Energy Consumption and Economic Growth: Evidence from an Asymmetric and Nonlinear ARDL Model. Energies, 14(16). https://doi.org/10.3390/en14165006
dc.relationKhosravi, V., Doulati Ardejani, F., Yousefi, S., & Aryafar, A. (2018). Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods. Geoderma, 318, 29–41. https://doi.org/10.1016/j.geoderma.2017.12.025
dc.relationKilian, L. (2009). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review, 99(3), 1053–1069. https://doi.org/10.1257/aer.99.3.1053
dc.relationKilian, L., & Park, C. (2009). The impact of oil price shocks on the U.S. stock market. International Economic Review, 50(4), 1267–1287. https://doi.org/10.1111/j.1468-2354.2009.00568.x
dc.relationKirikkaleli, D., & Güngör, H. (2021). Co-movement of commodity price indexes and energy price index: a wavelet coherence approach. Financial Innovation, 7(1), 15. https://doi.org/10.1186/s40854-021-00230-8
dc.relationKoch, N. (2014). Dynamic linkages among carbon, energy and financial markets: A smooth transition approach. Applied Economics, 46(7), 715–729. https://doi.org/10.1080/00036846.2013.854301
dc.relationKumar, S., Managi, S., & Matsuda, A. (2012). Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis. Energy Economics, 34(1), 215–226. https://doi.org/10.1016/j.eneco.2011.03.002
dc.relationKuzmanovic, M., Hatt, T., & Feuerriegel, S. (2021). Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies. In S. Roy, S. Pfohl, E. Rocheteau, G. A. Tadesse, L. Oala, F. Falck, Y. Zhou, L. Shen, G. Zamzmi, P. Mugambi, A. Zirikly, M. B. A. McDermott, & E. Alsentzer (Eds.), Proceedings of Machine Learning for Health (Vol. 158, pp. 143–155). PMLR. https://proceedings.mlr.press/v158/kuzmanovic21a.html
dc.relationLamouchi, R. A., & Alawi, S. M. (2020). Dynamic linkages between the oil spot, oil futures, and stock markets: Evidence from Dubai. International Journal of Energy Economics and Policy, 10(1), 377–383. https://doi.org/10.32479/ijeep.8705
dc.relationLe, T.-H., & Nguyen, C. P. (2019). Is energy security a driver for economic growth? Evidence from a global sample. Energy Policy, 129, 436–451. https://doi.org/10.1016/j.enpol.2019.02.038
dc.relationLe, 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. https://doi.org/10.1016/j.techfore.2020.120382
dc.relationLee, 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/https://doi.org/10.1016/j.najef.2020.101309
dc.relationLee, K., Ni, S., & Ratti, R. A. (1995). Oil Shocks and the Macroeconomy: The Role of Price Variability. The Energy Journal, 16(4). https://doi.org/10.5547/ISSN0195-6574-EJ-Vol16-No4-2
dc.relationLee, Y., & Yoon, S.-M. (2020). Dynamic spillover and hedging among carbon, biofuel and oil. Energies, 13(17). https://doi.org/10.3390/en13174382
dc.relationLi, H., Zhou, D., Hu, J., & Guo, L. (2022). Dynamic linkages among oil price, green bond, carbon market and low-carbon footprint company stock price: Evidence from the TVP-VAR model. Energy Reports, 8, 11249–11258. https://doi.org/10.1016/j.egyr.2022.08.230
dc.relationLi, Z., Ma, X., & Xin, H. (2017). Feature engineering of machine-learning chemisorption models for catalyst design. Catalysis Today, 280, 232–238. https://doi.org/10.1016/j.cattod.2016.04.013
dc.relationLichtenberger, A., Braga, J. P., & Semmler, W. (2022). Green Bonds for the Transition to a Low-Carbon Economy. Econometrics, 10(1). https://doi.org/10.3390/econometrics10010011
dc.relationLin, B., & Chen, Y. (2019). Dynamic linkages and spillover effects between CET market, coal market and stock market of new energy companies: A case of Beijing CET market in China. Energy, 172, 1198–1210. https://doi.org/10.1016/j.energy.2019.02.029
dc.relationLin, B., & Su, T. (2020). Mapping the oil price-stock market nexus researches: A scientometric review. International Review of Economics and Finance, 67, 133–147. https://doi.org/10.1016/j.iref.2020.01.007
dc.relationLin, J.-B., & Tsai, W. (2019). The relations of oil price change with fear gauges in global political and economic environment. Energies, 14(15). https://doi.org/10.3390/en12152982
dc.relationLiu, M. (2022). The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic. Economic Analysis and Policy, 75, 288–309. https://doi.org/10.1016/j.eap.2022.05.012
dc.relationLiu, X., Bouri, E., & Jalkh, N. (2021). Dynamics and Determinants of Market Integration of Green, Clean, Dirty Energy Investments and Conventional Stock Indices. Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.786528
dc.relationLiu, Z., Zhang, J., & Li, Y. (2022). Towards better time series prediction with model-independent, low-dispersion clusters of contextual subsequence embeddings. Knowledge-Based Systems, 235, 107641. https://doi.org/10.1016/j.knosys.2021.107641
dc.relationLotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323. http://www.jstor.org/stable/24529203
dc.relationLuo, R., Li, Y., Wang, Z., & Sun, M. (2022). Co-Movement between Carbon Prices and Energy Prices in Time and Frequency Domains: A Wavelet-Based Analysis for Beijing Carbon Emission Trading System. International Journal of Environmental Research and Public Health, 19(9). https://doi.org/10.3390/ijerph19095217
dc.relationMa, F., Wei, Y., Huang, D., & Zhao, L. (2013). Cross-correlations between West Texas Intermediate crude oil and the stock markets of the BRIC. Physica A: Statistical Mechanics and Its Applications, 392(21), 5356–5368. https://doi.org/10.1016/j.physa.2013.06.061
dc.relationMa, Z., Yan, Y., Wu, R., & Li, F. (2021). Research on the Correlation Between WTI Crude Oil Futures Price and European Carbon Futures Price. Frontiers in Energy Research, 9. https://doi.org/10.3389/fenrg.2021.735665
dc.relationMaghyereh, A., & Abdoh, H. (2022). Extreme dependence between structural oil shocks and stock markets in GCC countries. Resources Policy, 76. https://doi.org/10.1016/j.resourpol.2022.102626
dc.relationMaghyereh, A. I., Awartani, B., & Abdoh, H. (2019). The co-movement between oil and clean energy stocks: A wavelet-based analysis of horizon associations. Energy, 169, 895–913. https://doi.org/10.1016/j.energy.2018.12.039
dc.relationMahmood, H., Asadov, A., Tanveer, M., Furqan, M., & Yu, Z. (2022). Impact of Oil Price, Economic Growth and Urbanization on CO2 Emissions in GCC Countries: Asymmetry Analysis. Sustainability, 14(8), 4562.
dc.relationMahmood, H., & Furqan, M. (2021). Oil rents and greenhouse gas emissions: spatial analysis of Gulf Cooperation Council countries. Environment, Development and Sustainability, 23(4), 6215–6233. https://doi.org/10.1007/s10668-020-00869-w
dc.relationMaji, I. K., Habibullah, M. S., & Saari, M. Y. (2020). Does oil price shocks mitigate sectoral co2 emissions in malaysia? Evidence from ardl estimations. Kasetsart Journal of Social Sciences, 41(3), 633–640. https://doi.org/10.34044/j.kjss.2020.41.3.28
dc.relationMalik, M. I., & Rashid, A. (2017). Return and volatility spillover between sectoral stock and oil price: Evidence from pakistan stock exchange. Annals of Financial Economics, 12(2). https://doi.org/10.1142/S2010495217500075
dc.relationManjunath, S., & Halasuru Manjunath, P. (2023). A Novel Approach for Financial Markets Forecasting Using Deep Learning with Long Short Term Networks (pp. 456–462). https://doi.org/10.1007/978-3-031-17091-1_46
dc.relationMarimoutou, V., & Soury, M. (2015). Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model. Energy, 88, 417–429. https://doi.org/10.1016/j.energy.2015.05.060
dc.relationMarín-Rodríguez, N. J., González-Ruiz, J. D., & Botero Botero, S. (2022). Dynamic Co-Movements among Oil Prices and Financial Assets: A Scientometric Analysis. Sustainability, 14(19). https://doi.org/10.3390/su141912796
dc.relationMarín-Rodríguez, N. J., González-Ruiz, J. D., & Botero, S. (2022). Dynamic relationships among green bonds, CO2 emissions, and oil prices. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.992726
dc.relationMarín-Rodríguez, N. J., González-Ruiz, J. D., & Botero, S. (2023). A Wavelet Analysis of the Dynamic Connectedness among Oil Prices, Green Bonds, and CO2 Emissions. Risks, 11(1), 15. https://doi.org/10.3390/risks11010015
dc.relationMarquez-Cardenas, V., Gonzalez-Ruiz, J. D., & Duque-Grisales, E. (2021). Board gender diversity and firm performance: evidence from Latin America. Journal of Sustainable Finance and Investment. https://doi.org/10.1080/20430795.2021.2017256
dc.relationMarshall, A. (1890). Principles of Economics, 8th edn (1920). London, Mcmillan.
dc.relationMejia-Escobar, J. C., González-Ruiz, J. D., & Duque-Grisales, E. (2020). Sustainable financial products in the Latin America banking industry: Current status and insights. Sustainability (Switzerland), 12(14). https://doi.org/10.3390/su12145648
dc.relationMejía-Escobar, J. C., González-Ruiz, J. D., & Franco-Sepúlveda, G. (2021). Current state and development of green bonds market in the Latin America and the caribbean. Sustainability (Switzerland), 13(19). https://doi.org/10.3390/su131910872
dc.relationMelek, N. C. (2018). The response of US investment to oil price shocks: does the shale boom matter? Economic Review, Federal Reserve Bank of Kansas City Forthcoming.
dc.relationMensah, I. A., Sun, M., Gao, C., Omari-Sasu, A. Y., Zhu, D., Ampimah, B. C., & Quarcoo, A. (2019). Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach. Journal of Cleaner Production, 228, 161–174. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.04.281
dc.relationMensi, W. (2019). Global financial crisis and co-movements between oil prices and sector stock markets in Saudi Arabia: A VaR based wavelet. Borsa Istanbul Review, 19(1), 24–38. https://doi.org/10.1016/j.bir.2017.11.005
dc.relationMensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15–22. https://doi.org/10.1016/j.econmod.2013.01.023
dc.relationMensi, W., Hammoudeh, S., Reboredo, J. C., & Nguyen, D. K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach. Emerging Markets Review, 19, 1–17. https://doi.org/10.1016/j.ememar.2014.04.002
dc.relationMensi, W., Hammoudeh, S., & Yoon, S.-M. (2015). Structural breaks, dynamic correlations, asymmetric volatility transmission, and hedging strategies for petroleum prices and USD exchange rate. Energy Economics, 48, 46–60. https://doi.org/10.1016/j.eneco.2014.12.004
dc.relationMensi, W., Rehman, M. U., Maitra, D., Al-Yahyaee, K. H., & Vo, X. V. (2021). Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain. Resources Policy, 72. https://doi.org/10.1016/j.resourpol.2021.102062
dc.relationMesbah, M., Shahsavari, S., Soroush, E., Rahaei, N., & Rezakazemi, M. (2018). Accurate prediction of miscibility of CO2 and supercritical CO2 in ionic liquids using machine learning. Journal of CO2 Utilization, 25, 99–107. https://doi.org/10.1016/j.jcou.2018.03.004
dc.relationMitra, A., & Bhattacharjee, K. (2015). Financial interdependence of international stock markets: A literature review. Indian Journal of Finance, 9(5), 20–33. https://doi.org/10.17010/ijf/2015/v9i5/71447
dc.relationMohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., & Anastasiu, D. C. (2019). Stock Price Prediction Using News Sentiment Analysis. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 205–208. https://doi.org/10.1109/BigDataService.2019.00035
dc.relationMoomaw, W. R., & Unruh, G. C. (1997). Are environmental Kuznets curves misleading us? The case of CO2 emissions. Environment and Development Economics, 2(4), 451–463. https://doi.org/10.1017/S1355770X97000247
dc.relationMoral-Munoz, J. A., Arroyo-Morales, M., Herrera-Viedma, E., & Cobo, M. J. (2018). An Overview of Thematic Evolution of Physical Therapy Research Area From 1951 to 2013. Frontiers in Research Metrics and Analytics, 3. https://doi.org/10.3389/frma.2018.00013
dc.relationMorlet, J., Arens, G., Fourgeau, E., & Glard, D. (1982). Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media. Geophysics, 47(2), 203–221. https://doi.org/10.1190/1.1441328
dc.relationMoutinho, V., Madaleno, M., & Elheddad, M. (2020). Determinants of the Environmental Kuznets Curve considering economic activity sector diversification in the OPEC countries. Journal of Cleaner Production, 271, 122642. https://doi.org/10.1016/j.jclepro.2020.122642
dc.relationMujtaba, A., & Jena, P. K. (2021). Analyzing asymmetric impact of economic growth, energy use, FDI inflows, and oil prices on CO2 emissions through NARDL approach. Environmental Science and Pollution Research, 28(24), 30873–30886. https://doi.org/10.1007/s11356-021-12660-z
dc.relationMumu, J. R., Saona, P., Russell, H. I., & Azad, Md. A. K. (2021). Corporate governance and remuneration: a bibliometric analysis. Journal of Asian Business and Economic Studies, 28(4), 242–262. https://doi.org/10.1108/JABES-03-2021-0025
dc.relationNaeem, M. A., Bouri, E., Costa, M. D., Naifar, N., & Shahzad, S. J. H. (2021). Energy markets and green bonds: A tail dependence analysis with time-varying optimal copulas and portfolio implications. Resources Policy, 74, 102418. https://doi.org/10.1016/j.resourpol.2021.102418
dc.relationNaeem, M. A., Mbarki, I., Alharthi, M., Omri, A., & Shahzad, S. J. H. (2021). Did COVID-19 Impact the Connectedness Between Green Bonds and Other Financial Markets? Evidence From Time-Frequency Domain With Portfolio Implications. Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.657533
dc.relationNagayev, R., Disli, M., Inghelbrecht, K., & Ng, A. (2016). On the dynamic links between commodities and Islamic equity. Energy Economics, 58, 125–140. https://doi.org/10.1016/j.eneco.2016.06.011
dc.relationNandy, A., Zhu, J., Janet, J. P., Duan, C., Getman, R. B., & Kulik, H. J. (2019). Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation. ACS Catalysis, 9(9), 8243–8255. https://doi.org/10.1021/acscatal.9b02165
dc.relationNarayan, P. K., & Narayan, S. (2010). Modelling the impact of oil prices on Vietnam’s stock prices. Applied Energy, 87(1), 356–361. https://doi.org/10.1016/j.apenergy.2009.05.037
dc.relationNaser, H. (2015). Analysing the long-run relationship among oil market, nuclear energy consumption, and economic growth: An evidence from emerging economies. ENERGY, 89, 421–434. https://doi.org/10.1016/j.energy.2015.05.115
dc.relationNenonen, S., Koski, A., Lassila, A. P., & Lehikoinen, S. (2019). Towards low carbon economy - Green bond and asset development. IOP Conference Series: Earth and Environmental Science, 352(1). https://doi.org/10.1088/1755-1315/352/1/012028
dc.relationNguyen, 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. https://doi.org/10.1016/j.frl.2020.101739
dc.relationNi, J., & Xu, Y. (2023). Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method. Computational Economics, 61(1), 35–55. https://doi.org/10.1007/s10614-021-10198-3
dc.relationOmane-Adjepong, M., Alagidede, P., & Akosah, N. K. (2019). Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility. Physica A: Statistical Mechanics and Its Applications, 514, 105–120. https://doi.org/10.1016/j.physa.2018.09.013
dc.relationOmri, A., ben Mabrouk, N., & Sassi-Tmar, A. (2015). Modeling the causal linkages between nuclear energy, renewable energy and economic growth in developed and developing countries. Renewable & Sustainable Energy Reviews, 42, 1012–1022. https://doi.org/10.1016/j.rser.2014.10.046
dc.relationOmri, A., Daly, S., & Nguyen, D. K. (2015). A robust analysis of the relationship between renewable energy consumption and its main drivers. Applied Economics, 47(28), 2913–2923. https://doi.org/10.1080/00036846.2015.1011312
dc.relationOrlov, A. G. (2009). A cospectral analysis of exchange rate comovements during Asian financial crisis. Journal of International Financial Markets, Institutions and Money, 19(5), 742–758. https://doi.org/https://doi.org/10.1016/j.intfin.2008.12.004
dc.relationOsorio, S., Tietjen, O., Pahle, M., Pietzcker, R. C., & Edenhofer, O. (2021). Reviewing the Market Stability Reserve in light of more ambitious EU ETS emission targets. Energy Policy, 158. https://doi.org/10.1016/j.enpol.2021.112530
dc.relationOzturk, M. B. E., & Cavdar, S. C. (2021). The Contagion of Covid-19 Pandemic on The Volatilities of International Crude Oil Prices, Gold, Exchange Rates and Bitcoin. Journal of Asian Finance, Economics and Business, 8(3), 171–179. https://doi.org/10.13106/jafeb.2021.vol8.no3.0171
dc.relationPakel, C., Shephard, N., Sheppard, K., & Engle, R. F. (2021). Fitting Vast Dimensional Time-Varying Covariance Models. Journal of Business and Economic Statistics, 39(3), 652–668. https://doi.org/10.1080/07350015.2020.1713795
dc.relationPal, D., & Mitra, S. K. (2019). Oil price and automobile stock return co-movement: A wavelet coherence analysis. Economic Modelling, 76, 172–181. https://doi.org/10.1016/j.econmod.2018.07.028
dc.relationPanaretos, V. M., & Zemel, Y. (2019). Statistical Aspects of Wasserstein Distances. Annual Review of Statistics and Its Application, 6(1), 405–431. https://doi.org/10.1146/annurev-statistics-030718-104938
dc.relationPark, J., & Ratti, R. A. (2008). Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Economics, 30(5), 2587–2608. https://doi.org/10.1016/j.eneco.2008.04.003
dc.relationPark, O., & Seok, M. (2007). Selection of an appropriate model to predict plume dispersion in coastal areas. Atmospheric Environment, 41(29), 6095–6101. https://doi.org/10.1016/j.atmosenv.2007.04.010
dc.relationPata, U. K. (2021). Linking renewable energy, globalization, agriculture, CO2 emissions and ecological footprint in BRIC countries: A sustainability perspective. RENEWABLE ENERGY, 173, 197–208. https://doi.org/10.1016/j.renene.2021.03.125
dc.relationPatel, R., Goodell, J. W., Oriani, M. E., Paltrinieri, A., & Yarovaya, L. (2022). A bibliometric review of financial market integration literature. International Review of Financial Analysis, 80, 102035. https://doi.org/https://doi.org/10.1016/j.irfa.2022.102035
dc.relationPeña, A., Bonet, I., Lochmuller, C., Alejandro Patiño, H., Chiclana, F., & Góngora, M. (2018). A fuzzy credibility model to estimate the Operational Value at Risk using internal and external data of risk events. Knowledge-Based Systems, 159, 98–109. https://doi.org/10.1016/j.knosys.2018.06.007
dc.relationPeña, A., Bonet, I., Lochmuller, C., Chiclana, F., & Góngora, M. (2018). An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management. Expert Systems with Applications, 98, 11–26. https://doi.org/10.1016/j.eswa.2018.01.001
dc.relationPeña, A., Puerta, A., Bonet, I., Góngora, M., & Carafinni, F. (2020). Criterios para la configuración de plataformas de inteligencia aumentada para el mejoramiento de la sostenibilidad de cultivos agrícolas. In III Congreso Internacional de Ingeniería de Sistemas. Universidad de Lima.
dc.relationPeña, A., Tejada, J. C., Gonzalez-Ruiz, J. D., & Gongora, M. (2022). Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach. Sustainability, 14(11), 6668. https://doi.org/10.3390/su14116668
dc.relationPericoli, M., & Sbracia, M. (2003). A primer on financial contagion. Journal of Economic Surveys, 17(4), 571–608.
dc.relationPham, H. N. A., Ramiah, V., Moosa, N., Huynh, T., & Pham, N. (2018). The financial effects of Trumpism. Economic Modelling, 74, 264–274. https://doi.org/https://doi.org/10.1016/j.econmod.2018.05.020
dc.relationPiñeiro-Chousa, J., López-Cabarcos, M. Á., & Šević, A. (2022). Green bond market and Sentiment: Is there a switching Behaviour? Journal of Business Research, 141, 520–527. https://doi.org/10.1016/j.jbusres.2021.11.048
dc.relationPirgaip, B., & Dincergok, B. (2020). Economic policy uncertainty, energy consumption and carbon emissions in G7 countries: evidence from a panel Granger causality analysis. Environmental Science and Pollution Research, 27(24), 30050–30066. https://doi.org/10.1007/s11356-020-08642-2
dc.relationPrabheesh, K. P., Padhan, R., & Garg, B. (2020). COVID-19 and the Oil Price – Stock Market Nexus: Evidence From Net Oil-Importing Countries. Energy RESEARCH LETTERS, 1(2). https://doi.org/10.46557/001c.13745
dc.relationQuadrelli, R., & Peterson, S. (2007). The energy–climate challenge: Recent trends in CO2 emissions from fuel combustion. Energy Policy, 35(11), 5938–5952. https://doi.org/https://doi.org/10.1016/j.enpol.2007.07.001
dc.relationQureshi, S., Aftab, M., Bouri, E., & Saeed, T. (2020). Dynamic interdependence of cryptocurrency markets: An analysis across time and frequency. Physica A: Statistical Mechanics and Its Applications, 559, 125077. https://doi.org/10.1016/j.physa.2020.125077
dc.relationRai, K., & Garg, B. (2022). Dynamic correlations and volatility spillovers between stock price and exchange rate in BRIICS economies: evidence from the COVID-19 outbreak period. Applied Economics Letters, 29(8), 738–745. https://doi.org/10.1080/13504851.2021.1884835
dc.relationRangel, J. G., & Engle, R. F. (2012). The Factor-Spline-GARCH model for high and low frequency correlations. Journal of Business and Economic Statistics, 30(1), 109–124. https://doi.org/10.1080/07350015.2012.643132
dc.relationRannou, Y., Boutabba, M. A., & Barneto, P. (2021). Are Green Bond and Carbon Markets in Europe complements or substitutes? Insights from the activity of power firms. Energy Economics, 104. https://doi.org/10.1016/j.eneco.2021.105651
dc.relationRao, A., Gupta, M., Sharma, G. D., Mahendru, M., & Agrawal, A. (2022). Revisiting the financial market interdependence during COVID-19 times: a study of green bonds, cryptocurrency, commodities and other financial markets. International Journal of Managerial Finance, 18(4), 725–755. https://doi.org/10.1108/IJMF-04-2022-0165
dc.relationRasheed, M. Q., Haseeb, A., Adebayo, T. S., Ahmed, Z., & Ahmad, M. (2022). The long-run relationship between energy consumption, oil prices, and carbon dioxide emissions in European countries. Environmental Science and Pollution Research, 29(16), 24234–24247. https://doi.org/10.1007/s11356-021-17601-4
dc.relationReboredo, J. C. (2012). Modelling oil price and exchange rate co-movements. Journal of Policy Modeling, 34(3), 419–440. https://doi.org/10.1016/j.jpolmod.2011.10.005
dc.relationReboredo, J. C. (2013). Modeling EU allowances and oil market interdependence. Implications for portfolio management. Energy Economics, 36, 471–480. https://doi.org/10.1016/j.eneco.2012.10.004
dc.relationReboredo, 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
dc.relationReboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38–50. https://doi.org/10.1016/j.eneco.2018.05.030
dc.relationReboredo, J. C., & Rivera-Castro, M. A. (2014). Wavelet-based evidence of the impact of oil prices on stock returns. International Review of Economics and Finance, 29, 145–176. https://doi.org/10.1016/j.iref.2013.05.014
dc.relationReboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241–252. https://doi.org/10.1016/j.eneco.2016.10.015
dc.relationReboredo, 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
dc.relationReboredo, 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
dc.relationRen, C. (2022). Volatility Spillovers and Nexus across Oil, Gold, and Stock European Markets. American Business Review, 25(1), 52–185. https://doi.org/10.37625/abr.25.1.152-185
dc.relationRen, X., Dou, Y., Dong, K., & Li, Y. (2022). Information spillover and market connectedness: multi-scale quantile-on-quantile analysis of the crude oil and carbon markets. Applied Economics, 54(38), 4465–4485. https://doi.org/10.1080/00036846.2022.2030855
dc.relationRen, X., Li, Y., Qi, Y., & Duan, K. (2022). Asymmetric effects of decomposed oil-price shocks on the EU carbon market dynamics. Energy, 254. https://doi.org/10.1016/j.energy.2022.124172
dc.relationRen, X., Li, Y., yan, C., Wen, F., & Lu, Z. (2022). The interrelationship between the carbon market and the green bonds market: Evidence from wavelet quantile-on-quantile method. Technological Forecasting and Social Change, 179. https://doi.org/10.1016/j.techfore.2022.121611
dc.relationRen, X., Lu, Z., Cheng, C., Shi, Y., & Shen, J. (2019). On dynamic linkages of the state natural gas markets in the USA: Evidence from an empirical spatio-temporal network quantile analysis. Energy Economics, 80, 234–252. https://doi.org/10.1016/j.eneco.2019.01.001
dc.relationRen, X., Shao, Q., & Zhong, R. (2020). Nexus between green finance, non-fossil energy use, and carbon intensity: Empirical evidence from China based on a vector error correction model. Journal of Cleaner Production, 277, 122844. https://doi.org/10.1016/j.jclepro.2020.122844
dc.relationRen, Y.-S., Narayan, S., & Ma, C. (2021). Air quality, COVID-19, and the oil market: Evidence from China’s provinces. Economic Analysis And Policy, 72, 58–72. https://doi.org/10.1016/j.eap.2021.07.012
dc.relationReza, S., Ferreira, M. C., Machado, J. J. M., & Tavares, J. M. R. S. (2022). A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks. Expert Systems with Applications, 202, 117275. https://doi.org/10.1016/j.eswa.2022.117275
dc.relationRittler, D. (2012). Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis. Journal of Banking & Finance, 36(3), 774–785. https://doi.org/10.1016/j.jbankfin.2011.09.009
dc.relationRobledo, S., Osorio, G., & Lopez, C. (2014). Networking en pequeña empresa: una revisión bibliográfica utilizando la teoria de grafos. Revista Vínculos, 11(2), 6–16. https://doi.org/10.14483/2322939X.9664
dc.relationRodriguez-Fernandez, M. (2016). Social responsibility and financial performance: The role of good corporate governance. BRQ Business Research Quarterly, 19(2), 137–151. https://doi.org/10.1016/j.brq.2015.08.001
dc.relationRoy, R. P., & Roy, S. S. (2017). Financial contagion and volatility spillover: An exploration into Indian commodity derivative market. Economic Modelling, 67, 368–380.
dc.relationRoyal, S., Singh, K., & Chander, R. (2022). A nexus between renewable energy, FDI, oil prices, oil rent and CO<inf>2</inf> emission: panel data evidence from G7 economies. OPEC Energy Review, 46(2), 208–227. https://doi.org/10.1111/opec.12228
dc.relationSaboori, B., Al-mulali, U., bin Baba, M., & Mohammed, A. H. (2016). Oil-Induced environmental Kuznets curve in organization of petroleum exporting countries (OPEC). International Journal of Green Energy, 13(4), 408–416. https://doi.org/10.1080/15435075.2014.961468
dc.relationSadorsky, P. (1999). Oil price shocks and stock market activity. Energy Economics, 21(5), 449–469. https://doi.org/https://doi.org/10.1016/S0140-9883(99)00020-1
dc.relationSadorsky, P. (2001). Risk factors in stock returns of Canadian oil and gas companies. Energy Economics, 23(1), 17–28. https://doi.org/10.1016/S0140-9883(00)00072-4
dc.relationSadorsky, P. (2009). Renewable energy consumption, CO2 emissions and oil prices in the G7 countries. Energy Economics, 31(3), 456–462. https://doi.org/10.1016/j.eneco.2008.12.010
dc.relationSadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34(1), 248–255. https://doi.org/10.1016/j.eneco.2011.03.006
dc.relationSadorsky, P. (2014). Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Energy Economics, 43, 72–81. https://doi.org/10.1016/j.eneco.2014.02.014
dc.relationSaeed, 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
dc.relationSaeed, T., Bouri, E., & Tran, D. K. (2020). Hedging Strategies of Green Assets against Dirty Energy Assets. Energies, 13(12), 3141. https://doi.org/10.3390/en13123141
dc.relationSahu, P. K., Solarin, S. A., Al-mulali, U., & Ozturk, I. (2022). Investigating the asymmetry effects of crude oil price on renewable energy consumption in the United States. Environmental Science and Pollution Research, 29(1), 817–827. https://doi.org/10.1007/s11356-021-15577-9
dc.relationSalem, S. (2017). Key Commodity Markets: Dynamic Correlations & Volatilities in Time-Frequency Domain. University of Surrey (United Kingdom).
dc.relationSari, R., Hammoudeh, S., & Soytas, U. (2010). Dynamics of oil price, precious metal prices, and exchange rate. Energy Economics, 32(2), 351–362. https://doi.org/10.1016/j.eneco.2009.08.010
dc.relationSchmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
dc.relationSener, S. E. C., Sharp, J. L., & Anctil, A. (2018). Factors impacting diverging paths of renewable energy: A review. Renewable & Sustainable Energy Reviews, 81(2), 2335–2342. https://doi.org/10.1016/j.rser.2017.06.042
dc.relationShah, M. I., Foglia, M., Shahzad, U., & Fareed, Z. (2022). Green innovation, resource price and carbon emissions during the COVID-19 times: New findings from wavelet local multiple correlation analysis. Technological Forecasting and Social Change, 184. https://doi.org/10.1016/j.techfore.2022.121957
dc.relationShahzad, S. J. H., Mensi, W., Hammoudeh, S., Rehman, M. U., & Al-Yahyaee, K. H. (2018). Extreme dependence and risk spillovers between oil and Islamic stock markets. Emerging Markets Review, 34, 42–63. https://doi.org/10.1016/j.ememar.2017.10.003
dc.relationShankaranarayana, S. M., & Runje, D. (2019). ALIME: Autoencoder Based Approach for Local Interpretability (pp. 454–463). https://doi.org/10.1007/978-3-030-33607-3_49
dc.relationSharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70, 101496. https://doi.org/10.1016/j.irfa.2020.101496
dc.relationSingh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications, 76(18), 18569–18584. https://doi.org/10.1007/s11042-016-4159-7
dc.relationSingh, S., Bansal, P., & Bhardwaj, N. (2022). Correlation between geopolitical risk, economic policy uncertainty, and Bitcoin using partial and multiple wavelet coherence in P5 + 1 nations. Research in International Business and Finance, 63, 101756. https://doi.org/10.1016/j.ribaf.2022.101756
dc.relationSinghal, S., & Ghosh, S. (2016). Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models. Resources Policy, 50, 276–288. https://doi.org/10.1016/j.resourpol.2016.10.001
dc.relationSu, C. W., Chen, Y., Hu, J., Chang, T., & Umar, M. (2022). Can the green bond market enter a new era under the fluctuation of oil price? Economic Research-Ekonomska Istrazivanja. https://doi.org/10.1080/1331677X.2022.2077794
dc.relationSurya, E., & Wibowo, S. S. (2018). Empirical analysis of oil price volatility and stock returns in ASEAN-5 countries using DCC-GARCH. Pertanika Journal of Social Sciences and Humanities, 26(August), 251–263.
dc.relationSyed, A. A., Ahmed, F., Kamal, M. A., Ullah, A., & Ramos-Requena, J. P. (2022). Is There an Asymmetric Relationship between Economic Policy Uncertainty, Cryptocurrencies, and Global Green Bonds? Evidence from the United States of America. Mathematics, 10(5). https://doi.org/10.3390/math10050720
dc.relationTang, W., Wu, L., & Zhang, Z. (2010). Oil price shocks and their short- and long-term effects on the Chinese economy. Energy Economics, 32, S3–S14. https://doi.org/10.1016/j.eneco.2010.01.002
dc.relationTatar, A., Shokrollahi, A., Mesbah, M., Rashid, S., Arabloo, M., & Bahadori, A. (2013). Implementing Radial Basis Function Networks for modeling CO2-reservoir oil minimum miscibility pressure. Journal of Natural Gas Science and Engineering, 15, 82–92. https://doi.org/10.1016/j.jngse.2013.09.008
dc.relationTiwari, A. K., Aikins Abakah, E. J., Gabauer, D., & Dwumfour, R. A. (2021). Green Bond, Renewable Energy Stocks and Carbon Price: Dynamic Connectedness, Hedging and Investment Strategies during COVID-19 pandemic. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3897284
dc.relationTiwari, A. K., Aikins Abakah, E. J., Gabauer, D., & Dwumfour, R. A. (2022). Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID-19 pandemic: Implications for hedging and investments strategies. Global Finance Journal, 51. https://doi.org/10.1016/j.gfj.2021.100692
dc.relationTorrence, C., & Compo, G. P. (1998). A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79(1), 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
dc.relationTorrence, C., & Webster, P. J. (1999). Interdecadal Changes in the ENSO–Monsoon System. Journal of Climate, 12(8), 2679–2690. https://doi.org/10.1175/1520-0442(1999)012<2679:ICITEM>2.0.CO;2
dc.relationTroster, V., Shahbaz, M., & Uddin, G. S. (2018). Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis. Energy Economics, 70, 440–452. https://doi.org/10.1016/j.eneco.2018.01.029
dc.relationTsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2017). Using deep learning to detect price change indications in financial markets. 2017 25th European Signal Processing Conference (EUSIPCO), 2511–2515. https://doi.org/10.23919/EUSIPCO.2017.8081663
dc.relationTse, Y. K., & Tsui, A. K. C. (2002). A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics, 20(3), 351–362. https://doi.org/10.1198/073500102288618496
dc.relationTurhan, M. I., Sensoy, A., & Hacihasanoglu, E. (2014). A comparative analysis of the dynamic relationship between oil prices and exchange rates. Journal of International Financial Markets, Institutions and Money, 32(1), 397–414. https://doi.org/10.1016/j.intfin.2014.07.003
dc.relationUzar, U. (2020). Political economy of renewable energy: Does institutional quality make a difference in renewable energy consumption? Renewable Energy, 155, 591–603. https://doi.org/10.1016/j.renene.2020.03.172
dc.relationvan Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7
dc.relationVayá-Valcarce, E., & Frexedas, O. V. (2005). Financial contagion between economies: an exploratory spatial analysis. Estudios De Economia Aplicada, 23(1), 151–166.
dc.relationVieira, A. (2015). Predicting online user behaviour using deep learning algorithms. ArXiv Preprint.
dc.relationWang, C., Chen, Y., Zhang, S., & Zhang, Q. (2022). Stock market index prediction using deep Transformer model. Expert Systems with Applications, 208, 118128. https://doi.org/10.1016/j.eswa.2022.118128
dc.relationWang, S., & Wang, D. (2022). Exploring the Relationship Between ESG Performance and Green Bond Issuance. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.897577
dc.relationWang, W., Huang, Y., Wang, Y., & Wang, L. (2014). Generalized autoencoder: A neural network framework for dimensionality reduction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 490–497.
dc.relationWang, X., Li, J., & Ren, X. (2022). Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond. International Review of Financial Analysis, 83. https://doi.org/10.1016/j.irfa.2022.102306
dc.relationWang, Y., Wu, C., & Yang, L. (2013). Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries. Journal of Comparative Economics, 41(4), 1220–1239. https://doi.org/10.1016/j.jce.2012.12.004
dc.relationWei, P., Li, Y., Ren, X., & Duan, K. (2022). Crude oil price uncertainty and corporate carbon emissions. Environmental Science and Pollution Research, 29(2), 2385–2400. https://doi.org/10.1007/s11356-021-15837-8
dc.relationWen, X., Bouri, E., & Roubaud, D. (2017). Can energy commodity futures add to the value of carbon assets? Economic Modelling, 62, 194–206. https://doi.org/10.1016/j.econmod.2016.12.022
dc.relationWu, D., Wang, X., & Wu, S. (2022). A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction. Expert Systems with Applications, 207, 118006. https://doi.org/10.1016/j.eswa.2022.118006
dc.relationXuefeng, Z., Razzaq, A., Gokmenoglu, K. K., & Rehman, F. U. (2022). Time varying interdependency between COVID-19, tourism market, oil prices, and sustainable climate in United States: evidence from advance wavelet coherence approach. Economic Research-Ekonomska Istrazivanja, 35(1), 3337–3359. https://doi.org/10.1080/1331677X.2021.1992642
dc.relationYan, L., Wang, H., Athari, S. A., & Atif, F. (2022). Driving green bond market through energy prices, gold prices and green energy stocks: evidence from a non-linear approach. Economic Research-Ekonomska Istrazivanja. https://doi.org/10.1080/1331677X.2022.2049977
dc.relationYun, K. K., Yoon, S. W., & Won, D. (2023). Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection. Expert Systems with Applications, 213, 118803. https://doi.org/10.1016/j.eswa.2022.118803
dc.relationZaghdoudi, T. (2017). Oil prices, renewable energy, CO2 emissions and economic growth in OECD countries. Economics Bulletin, 37(3), 1844–1850. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028668466&partnerID=40&md5=d2530fbe0feb8e695f473eebbe41a2f3
dc.relationZhang, B., & Zhou, Y. (2022). Oil prices, emission permits trade of carbon, and the dependence between their quantiles. International Journal of Circuits, Systems and Signal Processing, 16, 38–45.
dc.relationZhang, Z., Zohren, S., & Roberts, S. (2019). DeepLOB: Deep Convolutional Neural Networks for Limit Order Books. IEEE Transactions on Signal Processing, 67(11), 3001–3012. https://doi.org/10.1109/TSP.2019.2907260
dc.relationZheng, Y., Zhou, M., & Wen, F. (2021). Asymmetric effects of oil shocks on carbon allowance price: Evidence from China. Energy Economics, 97. https://doi.org/10.1016/j.eneco.2021.105183
dc.relationZou, X. (2018). An analysis of the effect of carbon emission, GDP and international crude oil prices based on synthesis integration model. International Journal of Energy Sector Management, 12(4), 641–655. https://doi.org/10.1108/IJESM-10-2017-0013
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleDynamic co-movement analysis among oil prices, green bonds, and CO2 emissions, 2014-2022
dc.typeTrabajo de grado - Doctorado


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