Oil and renewable energy returns during pandemic
Jazyk angličtina Země Německo Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
19-22488S
Grantová Agentura České Republiky
PubMed
36346522
PubMed Central
PMC9641698
DOI
10.1007/s11356-022-23903-y
PII: 10.1007/s11356-022-23903-y
Knihovny.cz E-zdroje
- Klíčová slova
- Oil energy, Pandemic crisis, Renewable energy, Wavelet analysis,
- MeSH
- COVID-19 * MeSH
- lidé MeSH
- obnovitelná energie MeSH
- pandemie MeSH
- postup MeSH
- zdroje elektrické energie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
We explore the global interactions between oil and renewable energy returns during the Covid-19 pandemic between July 2019 and June 2020. Moreover, we reflect on market stress and global economic activity. In order to deal with challenges generated by exogenous shocks coming from financial, economic or pandemic areas, a battery of advanced time-frequency domain methods is applied, ranging from wavelet transformation and wavelet coherency to wavelet cohesion. The main finding shows that pandemic disease is veritable glue for the oil energy-renewable energy nexus, validating their coupling effect. Additionally, the emerging connection between renewable and financial developments is evidenced during the pandemic crisis, although the connection between oil and financial developments is still stronger. Finally, both renewable energy and oil markets have comparably strong relationships with the general global economic activity. The policy implications should follow direct adjustments in the renewable energy area, and subsidiary to cover the behaviour of agents on the capital markets.
Faculty of Business and Economics Mendel University in Brno Brno Czech Republic
Institute of Economic Research Slovak Academy of Sciences Bratislava Slovakia
Laboratoire d'Economie d'Orleans University of Orleans Orleans France
Zobrazit více v PubMed
Aguiar-Conraria L, Soares MJ. Oil and the macroeconomy: using wavelets to analyze old issues. Empirical Economics. 2011;40:645–655.
Aguiar-Conraria L, Azevedo N, Soares MJ. Using wavelets to decompose the time-frequency effects of monetary policy. Physica A. 2008;387:2863–2878.
Al Mamun M, Sohag K, Shahbaz M, Hammoudeh S. Financial markets, innovations and cleaner energy production in OECD countries. Energy Economics. 2018;72:236–254.
Baumeister C, Kilian L. Forty years of oil price fluctuations: why the price of oil may still surprise us. Journal of Economic Perspectives. 2016;30(1):139–160.
Chen W, Hamori S, Kinkyo T. Macroeconomic impacts of oil prices and underlying financial shocks. J Int Finan Markets Inst Money. 2014;29:1–12.
Ciner C (2001) Energy shocks and financial markets: nonlinear linkages. Stud Nonlinear Dyna Econ 5(3)
Croux C, Forni M, Reichlin L. A measure of co-movement for economic variables: theory and empirics. Rev Econ Stat. 2001;83:232–241.
Cunado J, de Gracia FP. Oil price shocks and stock market returns: evidence for some European countries. Energy Economics. 2014;42:365–377.
Demirer R, Ferrer R, Shahzad SJH. Oil price shocks, global financial markets and their connectedness. Energy Econ. 2020;88:104771.
Dutta A. Oil price uncertainty and clean energy stock returns: new evidence from crude oil volatility index. J Clean Prod. 2017;164:1157–1166.
Elsayed AH, Nasreen S, Tiwari AK. Time-varying co-movements between energy market and global financial markets: implication for portfolio diversification and hedging strategies. Energy Economics. 2020;90:104847.
Eroğlu H. Effects of Covid-19 outbreak on environment and renewable energy sector. Environ Dev Sustain. 2020;23:1–9. PubMed PMC
Farge M. Wavelet transforms and their applications to turbulence. Annu Rev Fluid Mech. 1992;24:395–457.
Ferrer R, Shahzad SJH, López R, Jareño F. Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics. 2018;76:1–20.
Fouquet R. Historical energy transitions: speed, prices and system transformation. Energy Res Soc Sci. 2016;22:7–12.
Grinsted A, Moore SJ, Jevrejeva C. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys. 2004;11:561–566.
Hache E. Do renewable energies improve energy security in the long run? International Economics. 2018;156:127–135.
Hudgins L, Friehe C, Mayer M. Wavelet transforms and atmospheric turbulence. Phys Rev Lett. 1993;71(20):3279–3282. PubMed
Huppmann D, Egging R. Market power, fuel substitution and infrastructure–a large-scale equilibrium model of global energy markets. Energy. 2014;75:483–500.
Kang W, Ratti RA, Yoon KH. Time-varying effect of oil market shocks on the stock market. J Bank Finance. 2015;61:150–163.
Khan MI, Yasmeen T, Shakoor A, Khan NB, Muhammad R. 2014 oil plunge: causes and impacts on renewable energy. Renew Sustain Energy Rev. 2017;68:609–622.
Kilian L, Park C. The impact of oil price shocks on the US stock market. Int Econ Rev. 2009;50(4):1267–1287.
Kim J, Park K. Financial development and deployment of renewable energy technologies. Energy Economics. 2016;59:238–250.
Lee D, Baek J. Stock prices of renewable energy firms: are there asymmetric responses to oil price changes? Economies. 2018;6(4):59.
Magazzino M, Mutascu M, Mele M, Sarkodie SA. Energy consumption and economic growth in Italy: a wavelet analysis. Energy Rep. 2021;7:1520–1528.
Monin PJ. The OFR Financial Stress Index. Risks. 2019;7(1):25.
Mutascu M. A time-frequency analysis of trade openness and CO2 emissions in France. Energy Policy. 2018;115:443–455.
Mutascu M, Sokic A. Trade openness – CO2 emissions nexus: a wavelet evidence from EU. Environ Sci Pollut Res. 2020;25:411–428.
Mutascu MI, Albulescu CT, Apergis N, Magazzino M. Do gasoline and diesel prices co-move? Evidence from the time–frequency domain. Environ Sci Pollut Res. 2022 doi: 10.1007/s11356-022-20517-2. PubMed DOI PMC
Nazlioglu S, Soytas U, Gupta R. Oil prices and financial stress: a volatility spillover analysis. Energy Policy. 2015;82:278–288.
Ng EKW, Chan JCL. Geophysical applications of partial wavelet coherence and multiple wavelet coherence. J Atmos Ocean Technol. 2012;29:1845–1853.
Norouzi N, de Rubens GZ, Choubanpishehzafar S, Enevoldsen P. When pandemics impact economies and climate change: exploring the impacts of COVID-19 on oil and electricity demand in China. Energy Res Soc Sci. 2020;68:101654. PubMed PMC
Office of Financial Research (2020) OFR index online dataset, U.S Department of the Treasury
Pomenkova J, Klejmova E, Kucerova Z. Cyclicality in lending activity of Euro area in pre- and post-2008 crisis: a local-adaptive-based testing of wavelets. Baltic Journal of Economics. 2019;19:155–175.
Qerimi D, Dimitrieska C, Vasilevska S, Rrecaj A. Modeling of the solar thermal energy use in urban areas. Civ Eng J. 2021;6(7):1349–1367.
Reboredo JC. Is there dependence and systemic risk between oil and renewable energy stock prices? Energy Econ. 2015;48:32–45.
Reboredo JC. Green bond and financial markets: co-movement, diversification and price spillover effects. Energy Econ. 2018;74:38–50.
Reboredo JC, Uddin GS. Do financial stress and policy uncertainty have an impact on the energy and metals markets? A quantile regression approach. Int Rev Econ Financ. 2016;43:284–298.
Reboredo JC, Rivera-Castro MA, Ugolini A. Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Econ. 2017;61:241–252.
Rua A. Measuring co-movement in the time-frequency space. J Macroecon. 2010;32(2):685–691.
Sadorsky P. Renewable energy consumption, CO2 emissions and oil prices in the G7 countries. Energy Econ. 2009;31(3):456–462.
Shahzad SJH, Bouri E, Kayani GM, Nasir RM, Kristoufek L. Are clean energy stocks efficient? Asymmetric multifractal scaling behaviour. Physica A: Statistical Mechanics and its Applications. 2020;550:124519.
Sharif A, Aloui C, Yarovaya L. COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: fresh evidence from the wavelet-based approach. Int Rev Financ Anal. 2020;70:101496. PubMed PMC
Song Y, Ji Q, Du YJ, Geng JB. The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets. Energy Economics. 2019;84:104564.
Tahiri FE, Chikh K, Khafallah M. Optimal management energy system and control strategies for isolated hybrid solar-wind-battery-diesel power system. Emerg Sci J. 2021;5(2):111–124.
Torrence C, Compo GP. A practical guide to wavelet analysis. Bull Am Meteor Soc. 1998;79:605–618.
Wei Y, Zhang J, Chen Y, Wang Y. The impacts of El Niño-southern oscillation on renewable energy stock markets: evidence from quantile perspective. Energy. 2022;260:124949.
Wen X, Guo Y, Wei Y, Huang D. How do the stock prices of new energy and fossil fuel companies correlate? Evidence from China. Energy Economics. 2014;41:63–75.
Yao CZ, Kuang PC. A study of lead–lag structure between international crude oil price and several financial markets. Physica A. 2019;531:121755.
Zhang D. Oil shocks and stock markets revisited: measuring connectedness from a global perspective. Energy Economics. 2017;62:323–333.
Zhang G, Du Z. Co-movements among the stock prices of new energy, high-technology and fossil fuel companies in China. Energy. 2017;135:249–256.