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The Spillover Effect from Oil and Gas Prices: Evidence of Energy Shocks from Diebold and Yilmaz Index

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Econometrics of Green Energy Handbook

Abstract

The current study contributes to the current debate on the energy-growth literature spillovers between crude prices, oil prices, and natural gas liquid composite prices. To this end, the recent novel Diebold and Yilmaz (2012) spillover index is utilized for daily realized data from January 2009 to October 31, 2019. The Diebold and Yilmaz index is employed given its uniqueness to highlight the following directional spillovers, total spillovers, pairwise spillover, and net spillover for the outlined variables. Further empirical investigation to accounts for both secular and cyclical properties is examined within the sampled framework. The study empirical results show a total spillover effect of 13.80% such that the contribution of shock from others is highest for liquefied natural gas (NGLC) price (43.2). The contribution of shocks to Brent price (7.5) and WTI price (3.0) was also received from others. Interestingly, the Brent price is observed to contribute the highest shock to others (41.4) considering the global adoption of the Brent crude oil as against the WTI which also contributes a shock of 12.9 to others. Based on these findings, several policy prescriptions were presented in the concluding section.

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Notes

  1. 1.

    Shale gas refers to natural gas that is trapped within shale formations. The combination of horizontal drilling and hydraulic fracturing has allowed access to large volumes of shale gas that were previously uneconomical to produce, and it has rejuvenated the natural gas industry, especially in the USA. It offers liquefaction developers a competitive advantage due to its competitive prices. Thanks to cheaper unconventional gas, the US gas prices have become more competitive resulting in significant LNG exports and liquefaction capacity hikes.

  2. 2.

    Energy reserves are estimated quantities of energy sources that analysis of geologic and engineering data demonstrates with reasonable certainty are recoverable under existing economic and operating conditions.

  3. 3.

    For the want of space, interested reader on the DY indices see the Studies of Diebold and Yilmaz (2009, 2012, 2014).

References

  • Adedoyin, F. F., Gumede, M. I., Bekun, F. V., Etokakpan, M. U., & Balsalobre-lorente, D. (2020). Modelling coal rent, economic growth and CO2 emissions: Does regulatory quality matter in BRICS economies? Science of the Total Environment, 710, 136284.

    Article  Google Scholar 

  • Alola, A. A. (2019). The trilemma of trade, monetary and immigration policies in the United States: Accounting for environmental sustainability. Science of the Total Environment, 658, 260–267.

    Article  Google Scholar 

  • Alola, A. A., Yalçiner, K., Alola, U. V., & Saint Akadiri, S. (2019). The role of renewable energy, immigration and real income in environmental sustainability target. Evidence from Europe largest states. Science of the Total Environment, 674, 307–315.

    Article  Google Scholar 

  • Álvarez-Díaz, M. (2019). Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods. Empirical Economics, 1–21.

    Google Scholar 

  • Caporin, M., Fontini, F., & Talebbeydokhti, E. (2019). Testing persistence of WTI and Brent long-run relationship after the shale oil supply shock. Energy Economics, 79, 21–31.

    Article  Google Scholar 

  • Caro, J. M. B., Golpe, A. A., Iglesias, J., & Vides, J. C. (2020). A new way of measuring the WTI–Brent spread. Globalization, shock persistence and common trends. Energy Economics, 85, 104546.

    Article  Google Scholar 

  • Diebold, 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.

    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.

    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.

    Google Scholar 

  • Erol, U., & Yu, E. S. (1987). On the causal relationship between energy and income for industrialized countries. The Journal of Energy and Development, 113–122.

    Google Scholar 

  • Geng, J. B., Ji, Q., & Fan, Y. (2016). How regional natural gas markets have reacted to oil price shocks before and since the shale gas revolution: A multi-scale perspective. Journal of Natural Gas Science and Engineering, 36, 734–746.

    Article  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.

    Google Scholar 

  • Kraft, J., & Kraft, A. (1978). On the relationship between energy and GNP. The Journal of Energy and Development, 401–403.

    Google Scholar 

  • Panagiotidis, T., & Rutledge, E. (2007). Oil and gas markets in the UK: Evidence from a cointegrating approach. Energy economics, 29(2), 329–347.

    Article  Google Scholar 

  • Pesaran, M. H., & Shin, Y. (1998). An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs, 31, 371–413.

    Google Scholar 

  • Soytas, U., & Sari, R. (2003). Energy consumption and GDP: Causality relationship in G-7 countries and emerging markets. Energy economics, 25(1), 33–37.

    Article  Google Scholar 

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Correspondence to Lucía Ibáñez-Luzón .

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Ibáñez-Luzón, L., Bekun, F.V., Alola, A.A., Balsalobre-Lorente, D. (2020). The Spillover Effect from Oil and Gas Prices: Evidence of Energy Shocks from Diebold and Yilmaz Index. In: Shahbaz, M., Balsalobre-Lorente, D. (eds) Econometrics of Green Energy Handbook. Springer, Cham. https://doi.org/10.1007/978-3-030-46847-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-46847-7_9

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