Finansal Stresin Fosil Enerji Emtiaları ile Yeşil Enerji Piyasaları Arasındaki Dinamik Bağlantılılığa Etkisi
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Date
2025
Authors
Efe C. Cagli
Pınar EVRİM MANDACI
Birce Tedik Kocakaya
Dilvin Taşkın
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Abstract
Bu makale FSI (Finansal Stres Endeksi) VIX (Volatilite Endeksi) ve EPU (Ekonomik Politika Belirsizliği) gibi seçili stres değişkenlerinin yeşil piyasalar (hisse senetleri ve tahviller) ile fosil enerji emtiaları arasındaki dinamik bağlantılılık üzerindeki etkilerini incelemeyi amaçlamaktadır. Bağlantılılığı ölçmek için TVP-VAR modelini ve bu stres değişkenlerinin 1 Kasım 2012'den 15 Kasım 2022'ye kadar bu bağlantı üzerindeki etkilerini araştırmak için Fourier Kümülatif Granger Nedensellik testini kullanıyoruz. Sonuçlar esas olarak kısa vadeli dinamiklerden kaynaklanan orta düzeyde getiri bağlantılılığı olduğunu gösteriyor ve bu da çeşitlendirmenin uzun vadeli yatırımlar için daha faydalı olabileceğini gösteriyor. COVID-19 salgını sırasında yüksek bağlantılılık gözlemliyoruz. Bağlantılılık su şirketi hisseleri hariç fosil enerji emtiaları arasında yüksek ancak yeşil hisse senedi ve tahvil piyasaları arasında düşüktür. Su hisselerinin piyasalar üzerinde önemli bir etkisi vardır bunu petrol takip eder. Nedensellik test sonuçlarımız FSI ve VIX'in bunların arasındaki bağlantılılığı etkilediğini göstermektedir.
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Keywords
financial stress, fosil enerji, yeşil piyasalar, Financial Economy, Time-Series Analysis, Financial Markets and Institutions, Finansal stress;Yeşil piyasalar;Fosil enerji;Bağlantılılık, green markets, connectedness, bağlantılılık, fossil energy, Finansal Ekonomi, Zaman Serileri Analizi, Finansal Piyasalar ve Kurumlar, HG1-9999, Green Economy, Yeşil Ekonomi, finansal stress, Financial stress;Green markets;Fossil energy;Connectedness, Finance
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Ang A. and Bekaert G. (2002). International asset allocation with regime shifts. The Review of Financial Studies 15(4) 1137-1187. https://doi.org/10.1093/rfs/15.4.1137Antonakakis N. Chatziantoniou I. and Gabauer D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management 13(4) 84. https://doi.org/10.3390/jrfm13040084Ari A. Arregui N. Black S. Celasun O. Iakova D. Mineshima A. Mylonas V. Parry I. Teodoru I. and Zhunussova K. (2022). Surging energy prices in Europe in the aftermath of the war: How to support the vulnerable and speed up the transition away from fossil fuels (IMF Working Paper No. 2022/152). Retrieved from https://ssrn.com/abstract=4184693Attarzadeh A. and Balcilar M. (2022). On the dynamic connectedness of the stock oil clean energy and technology markets. Energies 15(5) 1893. https://doi.org/10.3390/en15051893Baker S.R. Bloom N. and Davis S.J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics 131(4) 1593-1636. https://doi.org/10.1093/qje/qjw024Baruník J. and Křehlík T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics 16(2) 271-296. https://doi.org/10.1093/jjfinec/nby001Bondia R. Ghosh S. and Kanjilal K. (2016). International crude oil prices and the stock prices of clean energy and technology companies: Evidence from non-linear cointegration tests with unknown structural breaks. Energy 101 558-565. https://doi.org/10.1016/j.energy.2016.02.031Chatziantoniou I. Gabauer D. and Gupta R. (2023). Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach. Resources Policy 84 103729. https://doi.org/10.1016/j.resourpol.2023.103729Corbet S. Goodell J.W. and Günay S. (2020). Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19. Energy Economics 92 104978. https://doi.org/10.1016/j.eneco.2020.104978Das D. Maitra D. Dutta A. and Basu S. (2022). Financial stress and crude oil implied volatility: New evidence from continuous wavelet transformation framework. Energy Economics 115 106388. https://doi.org/10.1016/j.eneco.2022.106388Dawar I. Dutta A. Bouri E. and Saeed T. (2021). Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression. Renewable Energy 163 288-299. https://doi.org/10.1016/j.renene.2020.08.162Diebold F.X. and 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.2011.02.006Elliott G. Rothenberg T. and Stock J. (1996). Efficient tests for an autoregressive unit root. Econometrica 64 813-836. https://doi.org/10.2307/2171846Elsayed A.H. Downing G. Lau C.K.M. and Sheng X. (2024a). Exploring the role of oil shocks on the financial stability of Gulf Cooperation Council countries. International Journal of Finance & Economics 29(2) 1804-1819. https://doi.org/10.1002/ijfe.2757Elsayed A.H. Naifar N. Nasreen S. and Tiwari A.K. (2022). Dependence structure and dynamic connectedness between green bonds and financial markets: Fresh insights from time-frequency analysis before and during COVID-19 pandemic. Energy Economics 107 105842. https://doi.org/10.1016/j.eneco.2022.105842Elsayed A.H. Sohag K. and Sousa R.M. (2024b). Oil shocks and financial stability in MENA countries. Resources Policy 89 104653. https://doi.org/10.1016/j.resourpol.2024.104653Enders W. and Jones P. (2016). Grain prices oil prices and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics & Econometrics 20(4) 399-419. https://doi.org/10.1515/snde-2014-0101Enders W. and Lee J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters 117(1) 196-199. https://doi.org/10.1016/j.econlet.2012.04.081Fisher T.J. and Gallagher C.M. (2012). New weighted portmanteau statistics for time series goodness of fit testing. Journal of the American Statistical Association 107(498) 777-787. https://doi.org/10.1080/01621459.2012.688465Foglia M. and Angelini E. (2020). Volatility connectedness between clean energy firms and crude oil in the COVID-19 era. Sustainability 12(23) 9863. https://doi.org/10.3390/su12239863Fu Z. Chen Z. Sharif A. and Razi U. (2022). The role of financial stress oil gold and natural gas prices on clean energy stocks: Global evidence from extreme quantile approach. Resources Policy 78 102860. https://doi.org/10.1016/j.resourpol.2022.102860Hammoudeh S. Ajmi A.N. and Mokni K. (2020). Relationship between green bonds and financial and environmental variables: A novel time-varying causality. Energy Economics 92 104941. https://doi.org/10.1016/j.eneco.2020.104941Hanif W. Teplova T. Rodina V. Alomari M. and Mensi W. (2023). Volatility spillovers and frequency dependence between oil price shocks and green stock markets. Resources Policy 85 103860. https://doi.org/10.1016/j.resourpol.2023.103860He X. Mishra S. Aman A. Shahbaz M. Razzaq A. and Sharif A. (2021). The linkage between clean energy stocks and the fluctuations in oil price and financial stress in the US and Europe? Evidence from QARDL approach. Resources Policy 72 102021. https://doi.org/10.1016/j.resourpol.2021.102021Jarque C.M. and Bera A.K. (1980). Efficient tests for normality homoscedasticity and serial independence of regression residuals. Economics Letters 6(3) 255-259. https://doi.org/10.1016/0165-1765(80)90024-5Jiang Y. Wang J. Lie J. and Mo B. (2021). Dynamic dependence nexus and causality of the renewable energy stock markets on the fossil energy markets. Energy 233 121191. https://doi.org/10.1016/j.energy.2021.121191Kilian L. and Park C. (2009). The impact of oil price shocks on the US stock market. International Economic Review 50(4) 1267-1287. https://doi.org/10.1111/j.1468-2354.2009.00568.xKoop G. Pesaran M.H. and Potter S.M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics 74(1) 119-147. https://doi.org/10.1016/0304-4076(95)01753-4Lee C.C. Lee C.C. and Li Y.Y. (2021). Oil price shocks geopolitical risks and green bond market dynamics. The North American Journal of Economics and Finance 55 101309. https://doi.org/10.1016/j.najef.2020.101309Lucey B. and Ren B. (2023). Time-varying tail risk connectedness among sustainability-related products and fossil energy investments. Energy Economics 126 106812. https://doi.org/10.1016/j.eneco.2023.106812Mensi W. Naeem M.A. Vo X.V. and Kang S.H. (2022). Dynamic and frequency spillovers between green bonds oil and G7 stock markets: Implications for risk management. Economic Analysis and Policy 73 331-344. https://doi.org/10.1016/j.eap.2021.11.015Naeem M.A. Adekoya O.B. and Oliyide J.A. (2021b). Asymmetric spillovers between green bonds and commodities. Journal of Cleaner Production 314 128100. https://doi.org/10.1016/j.jclepro.2021.128100Naeem M.A. Bouri E. Costa M.D. Naifar N. and Shahzad S.J. (2021a). 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.Naeem M.A. Nguyen T.T. Nepal R. Ngo Q.T. and Taghizadeh–Hesary F. (2021c). Asymmetric relationship between green bonds and commodities: Evidence from extreme quantile approach. Finance Research Letters 43 101983. https://doi.org/10.1016/j.frl.2021.101983Nazlioglu S. Gormus N.A. and Soytas U. (2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics 60 168-175. https://doi.org/10.1016/j.eneco.2016.09.009Nazlioglu S. Gormus A. and Soytas U. (2019). Oil prices and monetary policy in emerging markets: Structural shifts in causal linkages. Emerging Markets Finance and Trade 55(1) 105-117. https://doi.org/10.1080/1540496X.2018.1434072Nazlioglu S. Soytas U. and Gupta R. (2015). Oil prices and financial stress: A volatility spillover analysis. Energy Policy 82 278-288. https://doi.org/10.1016/j.enpol.2015.01.003Nguyen T.T. Naeem M.A. Balli F. Balli H.O. and Vo X.V. (2021). Time-frequency comovement among green bonds stocks commodities clean energy and conventional bonds. Finance Research Letters 40 101739. https://doi.org/10.1016/j.frl.2020.101739Park J. and Ratti R.A. (2008). Oil price shocks and stock markets in the US and 13 European countries. Energy Economics 30(5) 2587-2608. https://doi.org/10.1016/j.eneco.2008.04.003Pesaran H.H. and Shin Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters 58(1) 17-29. https://doi.org/10.1016/S0165-1765(97)00214-0Pham L. (2019). Do all clean energy stocks respond homogeneously to oil price? Energy Economics 81 355-379. https://doi.org/10.1016/j.eneco.2019.04.010Reboredo 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.2014.12.009Reboredo 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.2018.05.030Reboredo J.C. and Uddin G.S. (2016). Do financial stress and policy uncertainty impact the energy and metals markets? A quantile regression approach. International Review of Economics & Finance 43 284-298. https://doi.org/10.1016/j.iref.2015.10.043Reboredo J.C. Ugolini A. and Aiube F.A. (2020). Network connectedness of green bonds and asset classes. Energy Economics 86 104629. https://doi.org/10.1016/j.eneco.2019.104629Ren X. Xiao Y. Duan K. and Urquhart A. (2024). Spillover effects between fossil energy and green markets: Evidence from informational inefficiency. Energy Economics 131 107317. https://doi.org/10.1016/j.eneco.2024.107317Sadorsky P. (1999). Oil price shocks and stock market activity. Energy economics 21(5) 449-469. https://doi.org/10.1016/S0140-9883(99)00020-1Saeed T. Bouri E. and 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.2020.105017Shinwari R. Yangjie W. Payab A.H. Kubiczek J. and Dördüncü H. (2022). What drives investment in renewable energy resources? Evaluating the role of natural resources volatility and economic performance for China. Resources Policy 77 102712. https://doi.org/10.1016/j.resourpol.2022.102712Song Y. Ji Q. Du Y.J. and Geng J.B. (2019). The dynamic dependence of fossil energy investor sentiment and renewable energy stock markets. Energy Economics 84 104564. https://doi.org/10.1016/j.eneco.2019.104564Stiassny A. (1996). A spectral decomposition for structural VAR models. Empirical Economics 21 535-555. Retrieved from https://doi.org/10.1007/BF01180700Tang C. Aruga K. and Hu Y. (2023). The dynamic correlation and volatility spillover among green bonds clean energy stock and fossil fuel market. Sustainability 15(8) 6586. https://doi.org/10.3390/su15086586Tiwari A.K. Abakah E.J.A. Dwumfour R.A. and Gil-Alana L.A. (2024). Correlation and dependence between oil prices stock returns policy uncertainty and financial stress during COVID-19 Pandemic: New evidence from a multicountry analysis using cross-quantilogram method. In C.F. Lee (Ed.) Handbook of investment analysis portfolio management and financial derivatives (pp. 283-320). https://doi.org/10.1142/9789811269943_0009Tiwari A.K. Abakah E.J. Gabauer D. and 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 100692. https://doi.org/10.1016/j.gfj.2021.100692Toda H.Y. and Yamamoto T. (1995) Statistical inference in vector autoregressions with possibly integrated processes Journal of Econometrics 66(1–2) 225–250. https://doi.org/10.1016/0304-4076(94)01616-8Umar M. Farid S. and Naeem M.A. (2022). Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial oil and pandemic crisis. Energy 240 122702. https://doi.org/10.1016/j.energy.2021.122702Umar Z. Hadhri S. Abakah E.J.A. Usman M. and Umar M. (2024). Return and volatility spillovers among oil price shocks and international green bond markets. Research in International Business and Finance 69 102254. https://doi.org/10.1016/j.ribaf.2024.102254Wang Y. Wu C. and 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.irfa.2022.102306Zhou W. Chen Y. and Chen J. (2022). Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic. Energy 256 124580. https://doi.org/10.1016/j.energy.2022.124580
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