Abstract
This paper explores the dependence structure among carbon prices, fossil energy prices and renewable energy price using the conditional vine copula approach. The major two contributions in our study are following. First, regarding technological innovation and development of new technologies in alternative energy sources, we consider renewable energy index into our study. Second, we simultaneously investigate the multivariate dependence among all variables so that each of them can interact with the others based on a rich variety of bivariate copula functions. We mainly find that there is a reliable and positive link between coal and oil prices, and between gas and oil prices. And we corroborate that variations in the carbon prices affect the coal price returns positively, though the association is usually found to be statistically insignificant. Moreover, carbon prices affect the renewable energy stock returns positively and strongly significant. Such findings we suggest that policymakers could adopt effective measures and action plan to elevate carbon prices so that the emission market could provide incentives to shift from conventional fossil fuels to clean and low-carbon energy sources.
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Zhou, Y., Liu, J., Sirisrisakulchai, J., Sriboonchitta, S. (2020). Measurements of the Conditional Dependence Structure Among Carbon, Fossil Energy and Renewable Energy Prices: Vine Copula Based GJR-GARCH Model. In: Huynh, VN., Entani, T., Jeenanunta, C., Inuiguchi, M., Yenradee, P. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2020. Lecture Notes in Computer Science(), vol 12482. Springer, Cham. https://doi.org/10.1007/978-3-030-62509-2_27
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DOI: https://doi.org/10.1007/978-3-030-62509-2_27
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