Finansal Stresin Fosil Enerji Emtiaları ile Yeşil Enerji Piyasaları Arasındaki Dinamik Bağlantılılığa Etkisi

Abstract

Bu makale, FSI (Finansal Stres Endeksi), VIX (Dalgalanma Endeksi) ve EPU (Ekonomik Politika Belirsizliği) gibi seçilmiş stres değişkenlerinin yeşil piyasalar (hisse senetleri ve tahviller) ile fosil enerji emtiaları arasındaki dinamik bağlantılılık üzerindeki etkilerini incelemeyi amaçlamaktadır. İlk olarak, bağlantılılığı ölçmek için TVP-VAR modelini kullanıyoruz. Ardından, Fourier Kümülatif Granger Nedensellik testi, bu stres değişkenlerinin bu bağlantılılık üzerindeki etkilerini 1 Kasım 2012'den 15 Kasım 2022'ye kadar araştırmak için kullanılacaktır. Sonuçlar, esas olarak kısa vadeli dinamiklerden kaynaklanan bunlar arasında orta düzeyde getiri bağlantılılığı olduğunu ve çeşitlendirmenin uzun vadeli yatırımlar için daha faydalı olabileceğini göstermektedir. Ek olarak, sonuçlar COVID-19 salgını sırasında yüksek bağlantılılık olduğunu göstermektedir. Sonuçlar, su şirketi hisseleri hariç, fosil enerji emtiaları arasında yüksek bağlantılılık, yeşil hisse senedi ve yeşil tahvil piyasaları arasında düşük bağlantılılık göstermektedir. Su hisselerinin piyasalar üzerinde daha önemli bir etkisi olduğunu, bunu petrolün izlediğini gözlemliyoruz. Nedensellik test sonuçlarımız, FSI ve VIX'in bu iki piyasa arasındaki bağlantılılığı etkilediğini, ancak bağlantılılığın tüm değişkenleri etkilediğini göstermektedir. Sonuçlarımız yatırımcılar ve politika yapıcılar için önemli çıkarımlar sağlamaktadır.

Keywords

• Finansal stress
• Yeşil piyasalar
• Fosil enerji
• Bağlantılılık

Project Number

SBA-2022-2908

The Influence of Financial Stress on Dynamic Connectedness between Fossil Energy Commodities and Green Energy Markets

Abstract

This paper aims to examine the impacts of selected stress variables, such as FSI (Financial Stress Index), VIX (Volatility Index), and EPU (Economic Policy Uncertainty), on dynamic connectedness between green markets (stocks and bonds) and fossil energy commodities. First, we employ the TVP-VAR model to measure connectedness. Then, the Fourier Cumulative Granger Causality test will be used to investigate the impacts of these stress variables on this connectedness from November 1, 2012, to November 15, 2022. The results indicate moderate return connectedness between these, mainly from short-term dynamics, suggesting that diversification may be more beneficial for long-term investments. In addition, the results indicate high connectedness during the COVID-19 pandemic. The results show high connectedness among fossil energy commodities but low connectedness among green stock and green bond markets, except for water company stocks. We observe a more significant impact of water stocks on markets, followed by oil. Our causality test results indicate that the FSI and VIX impact the connectedness between these two markets, but the connectedness influences all variables. Our results provide important implications for investors and policymakers.

Keywords

• Financial stress
• Green markets
• Fossil energy
• Connectedness

Supporting Institution

The Scientific Research Projects Coordination Unit of Dokuz Eylül University within the Research Universities Support Program (ADEP) scope by the Council of Higher Education (YÖK). Project Number: SBA-2022-2908.

Project Number

SBA-2022-2908

References

1. 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/15.4.1137
2. Antonakakis, 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/jrfm13040084
3. Ari, 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=4184693
4. Attarzadeh, 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/en15051893
5. Baker, 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/qjw024
6. Baruník, 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/nby001
7. Bondia, 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.2016.02.031
8. Chatziantoniou, 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.2023.103729

9. Corbet, 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.2020.104978
10. Das, 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.2022.106388
11. Dawar, 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.2020.08.162
12. Diebold, 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.2011.02.006
13. Elliott, G., Rothenberg, T. and Stock, J. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64, 813-836. https://doi.org/10.2307/2171846
14. Elsayed, 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.2757
15. Elsayed, 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.2022.105842
16. Elsayed, 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.2024.104653
17. Enders, 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-2014-0101
18. Enders, 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.2012.04.081
19. Fisher, 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/10.1080/01621459.2012.688465
20. Foglia, 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/su12239863
21. Fu, 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.2022.102860
22. Hammoudeh, 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.2020.104941
23. Hanif, 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.2023.103860
24. He, 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.2021.102021
25. Jarque, 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)90024-5
26. Jiang, 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.2021.121191
27. Kilian, 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.x
28. Koop, 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)01753-4
29. Lee, 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.2020.101309
30. Lucey, 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.2023.106812
31. Mensi, 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.2021.11.015
32. Naeem, 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.2021.128100
33. Naeem, 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.
34. 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.2021.101983
35. Nazlioglu, 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.2016.09.009
36. Nazlioglu, 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.2018.1434072
37. Nazlioglu, 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.2015.01.003
38. Nguyen, 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.2020.101739
39. Park, 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.2008.04.003
40. Pesaran, 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)00214-0
41. Pham, L. (2019). Do all clean energy stocks respond homogeneously to oil price? Energy Economics, 81, 355-379. https://doi.org/10.1016/j.eneco.2019.04.010
42. Reboredo, 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
43. Reboredo, 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
44. Reboredo, 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.2015.10.043
45. Reboredo, 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.2019.104629
46. Ren, 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.2024.107317
47. Sadorsky, P. (1999). Oil price shocks and stock market activity. Energy economics, 21(5), 449-469. https://doi.org/10.1016/S0140-9883(99)00020-1
48. Saeed, 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.2020.105017
49. Shinwari, 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.2022.102712
50. Song, 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.2019.104564
51. Stiassny, A. (1996). A spectral decomposition for structural VAR models. Empirical Economics, 21, 535-555. Retrieved from https://doi.org/10.1007/BF01180700
52. Tang, 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/su15086586
53. Tiwari, 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_0009
54. Tiwari, 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.2021.100692
55. Toda, 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)01616-8
56. Umar, 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.2021.122702
57. Umar, 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.2024.102254
58. Wang, 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.2022.102306
59. Zhou, 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