The rapid expansion of renewable energy sources and their integration into the energy mix has generated scholarly interest in comprehending the interplay between renewable and conventional energy markets. This research aims to examine the (a)symmetric volatility spillover between the oil market and various regional renewable energy stock markets, namely the US, Europe and Asia. To achieve this objective, we employ the time-varying parameter vector autoregressive-based connectedness (TVP-VAR) approach, which allows analysing the interconnection and transmission of shocks between the different markets. Based on an analysis of daily data relative to the different regional renewable energy stock markets and international oil prices, the findings suggest the presence of a dynamic volatility connectedness between the green and brown energy stock markets. The extent of connectedness is contingent upon the specific regional renewable energy market under consideration. Moreover, the decomposition of the volatility series into good and bad volatility emphasizes an asymmetric pattern, which becomes more pronounced during periods of major events. On average, the oil market and the Asian renewable energy stock market are net receivers of volatility shocks. In contrast, the US and European renewable energy stock markets are net transmitters of shocks. Our findings provide investors with valuable insights for portfolio design and risk management decisions.
Citation: Mohammed Alharbey, Turki Mohammed Alfahaid, Ousama Ben-Salha. Asymmetric volatility spillover between oil prices and regional renewable energy stock markets: A time-varying parameter vector autoregressive-based connectedness approach[J]. AIMS Mathematics, 2023, 8(12): 30639-30667. doi: 10.3934/math.20231566
The rapid expansion of renewable energy sources and their integration into the energy mix has generated scholarly interest in comprehending the interplay between renewable and conventional energy markets. This research aims to examine the (a)symmetric volatility spillover between the oil market and various regional renewable energy stock markets, namely the US, Europe and Asia. To achieve this objective, we employ the time-varying parameter vector autoregressive-based connectedness (TVP-VAR) approach, which allows analysing the interconnection and transmission of shocks between the different markets. Based on an analysis of daily data relative to the different regional renewable energy stock markets and international oil prices, the findings suggest the presence of a dynamic volatility connectedness between the green and brown energy stock markets. The extent of connectedness is contingent upon the specific regional renewable energy market under consideration. Moreover, the decomposition of the volatility series into
| [1] | United Nations, Theme report on energy transition—towards the achievement of SDG 7 and net-zero emissions, New York, 2021. |
| [2] | D. Yergin, Bumps in the energy transition, Financ. Dev., 59 (2022), 8–13. |
| [3] |
O. Ben-Salha, A. Hakimi, T. Zaghdoudi, H. Soltani, M. Nsaibi, assessing the impact of fossil fuel prices on renewable energy in China using the novel dynamic ARDL simulations approach, Sustainability, 14 (2022), 10439. https://doi.org/10.3390/su141610439 doi: 10.3390/su141610439
|
| [4] |
S. Kumar, S. Managi, A. Matsuda, Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis, Energy Econ., 34 (2012), 215–226. https://doi.org/10.1016/j.eneco.2011.03.002 doi: 10.1016/j.eneco.2011.03.002
|
| [5] |
X. Zhao, Do the stock returns of clean energy corporations respond to oil price shocks and policy uncertainty? J. Econ. Struct., 9 (2020), 1–16. https://doi.org/10.1186/s40008-020-00229-x doi: 10.1186/s40008-020-00229-x
|
| [6] |
R. Li, G. C. Leung, The relationship between energy prices, economic growth and renewable energy consumption: Evidence from Europe, Energy Rep., 7 (2021), 1712–1719. https://doi.org/10.1016/j.egyr.2021.03.030 doi: 10.1016/j.egyr.2021.03.030
|
| [7] |
I. A. Mensah, M. Sun, C. Gao, A. Y. Omari-Sasu, D. Zhu, B. C. Ampimah, et al., Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach, J. Clean. Prod., 228 (2019), 161–174. https://doi.org/10.1016/j.jclepro.2019.04.281 doi: 10.1016/j.jclepro.2019.04.281
|
| [8] |
S. Mukhtarov, J. I. Mikayilov, S. Humbatova, V. Muradov, Do high oil prices obstruct the transition to renewable energy consumption? Sustainability, 12 (2020), 4689. https://doi.org/10.3390/su12114689 doi: 10.3390/su12114689
|
| [9] |
M. Murshed, M. M. Tanha, Oil price shocks and renewable energy transition: Empirical evidence from net oil-importing South Asian economies, Energy Ecol. Environ., 6 (2021), 183–203. https://doi.org/10.1007/s40974-020-00168-0 doi: 10.1007/s40974-020-00168-0
|
| [10] |
R. Karacan, S. Mukhtarov, İ. Barış, A. İşleyen, M. E. Yardımcı, The impact of oil price on transition toward renewable energy consumption? Evidence from Russia, Energies, 14 (2021), 2947. https://doi.org/10.3390/en14102947 doi: 10.3390/en14102947
|
| [11] |
B. Raggad, Time varying causal relationship between renewable energy consumption, oil prices and economic activity: New evidence from the United States, Resour. Policy, 74 (2021), 102422. https://doi.org/10.1016/j.resourpol.2021.102422 doi: 10.1016/j.resourpol.2021.102422
|
| [12] |
P. K. Sahu, S. A. Solarin, U. Al-Mulali, I. Ozturk, Investigating the asymmetry effects of crude oil price on renewable energy consumption in the United States, Environ. Sci. Pollut. R., 29 (2022), 817–827. https://doi.org/10.1007/s11356-021-15577-9 doi: 10.1007/s11356-021-15577-9
|
| [13] |
S. Hammoudeh, K. Mokni, O. Ben-Salha, A. N. Ajmi, Distributional predictability between oil prices and renewable energy stocks: Is there a role for the COVID-19 pandemic? Energy Econ., 103 (2021), 105512. https://doi.org/10.1016/j.eneco.2021.105512 doi: 10.1016/j.eneco.2021.105512
|
| [14] |
J. C. Reboredo, Is there dependence and systemic risk between oil and renewable energy stock prices? Energy Econ., 48 (2015), 32–45. https://doi.org/10.1016/j.eneco.2014.12.009 doi: 10.1016/j.eneco.2014.12.009
|
| [15] |
A. BenSaïda, Good and bad volatility spillovers: An asymmetric connectedness, J. Financ. Mark., 43 (2019), 78–95. https://doi.org/10.1016/j.finmar.2018.12.005 doi: 10.1016/j.finmar.2018.12.005
|
| [16] |
S. Ghosh, COVID-19, clean energy stock market, interest rate, oil prices, volatility index, geopolitical risk nexus: Evidence from quantile regression, J. Econ. Dev., 2022. https://doi.org/10.1108/JED-04-2022-0073 doi: 10.1108/JED-04-2022-0073
|
| [17] |
J. C. Reboredo, M. A. Rivera-Castro, A. Ugolini, Wavelet-based test of co-movement and causality between oil and renewable energy stock prices, Energy Econ., 61 (2017), 241–252. https://doi.org/10.1016/j.eneco.2016.10.015 doi: 10.1016/j.eneco.2016.10.015
|
| [18] |
F. Horky, M. Mutascu, J. Fidrmuc, Oil and renewable energy returns during pandemic, Environ. Sci. Pollut. Res., 30 (2023), 25836–25850. https://doi.org/10.1007/s11356-022-23903-y doi: 10.1007/s11356-022-23903-y
|
| [19] | L. T. Ha, Dynamic connectedness between green energy and carbon risk during Russia-Ukraine conflict: New evidence from a wavelet analysis, Environ. Sci. Pollut. R., 2023, 1–18. |
| [20] |
W. Hanif, T. Teplova, V. Rodina, M. Alomari, W. Mensi, Volatility spillovers and frequency dependence between oil price shocks and green stock markets, Resour. Policy, 85 (2023), 103860. https://doi.org/10.1016/j.resourpol.2023.103860 doi: 10.1016/j.resourpol.2023.103860
|
| [21] |
L. Pham, Do all clean energy stocks respond homogeneously to oil price? Energy Econ., 81 (2019), 355–379. https://doi.org/10.1016/j.eneco.2019.04.010 doi: 10.1016/j.eneco.2019.04.010
|
| [22] |
Y. Song, Q. Ji, Y. J. Du, J. B. Geng, The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets, Energy Econ., 84 (2019), 104564. https://doi.org/10.1016/j.eneco.2019.104564 doi: 10.1016/j.eneco.2019.104564
|
| [23] |
T. Liu, S. Hamori, Spillovers to renewable energy stocks in the US and Europe: Are they different? Energies, 13 (2020), 3162. https://doi.org/10.3390/en13123162 doi: 10.3390/en13123162
|
| [24] |
L. Zhao, W. He, A. Wang, F. Zhu, Time and frequency spillovers between the green economy and traditional energy markets, Systems, 11 (2023), 153. https://doi.org/10.3390/systems11030153 doi: 10.3390/systems11030153
|
| [25] |
I. Henriques, P. Sadorsky, Oil prices and the stock prices of alternative energy companies, Energy Economics, 30 (2008), 998–1010. https://doi.org/10.1016/j.eneco.2007.11.001 doi: 10.1016/j.eneco.2007.11.001
|
| [26] |
F. Fuentes, R. Herrera, Dynamics of connectedness in clean energy stocks, Energies, 13 (2020), 3705. https://doi.org/10.3390/en13143705 doi: 10.3390/en13143705
|
| [27] |
F. X. Diebold, K. Yilmaz, Better to give than to receive: Predictive directional measurement of volatility spillovers, Int. J. Forecasting, 28 (2012), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006 doi: 10.1016/j.ijforecast.2011.02.006
|
| [28] |
F. X. Diebold, K. Yılmaz, On the network topology of variance decompositions: measuring the connectedness of financial firms, J. Econometrics, 182 (2014). https://doi.org/10.1016/j.jeconom.2014.04.012 doi: 10.1016/j.jeconom.2014.04.012
|
| [29] |
S. Managi, T. Okimoto, Does the price of oil interact with clean energy prices in the stock market? JPN World Econ., 27 (2013), 1–9. https://doi.org/10.1016/j.japwor.2013.03.003 doi: 10.1016/j.japwor.2013.03.003
|
| [30] | W. Ahmad, An analysis of directional spillover between crude oil prices and stock prices of clean energy and technology companies, Res. Int. Bus. Financ., 42 (2017), 376–389. |
| [31] |
T. Xia, Q. Ji, D. Zhang, J. Han, Asymmetric and extreme influence of energy price changes on renewable energy stock performance, J. Clean. Prod., 241 (2019), 118338. https://doi.org/10.1016/j.jclepro.2019.118338 doi: 10.1016/j.jclepro.2019.118338
|
| [32] |
G. Koop, D. Korobilis, A new index of financial conditions, Eur. Econ. Rev., 71 (2014), 101–116. https://doi.org/10.1016/j.euroecorev.2014.07.002 doi: 10.1016/j.euroecorev.2014.07.002
|
| [33] |
G. N. Apostolakis, C. Floros, K. Gkillas, M. Wohar, Financial stress, economic policy uncertainty, and oil price uncertainty, Energy Econ., 104 (2021), 105686. https://doi.org/10.1016/j.eneco.2021.105686 doi: 10.1016/j.eneco.2021.105686
|
| [34] |
N. Antonakakis, I. Chatziantoniou, D. Gabauer, Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions, J. Risk Financ. Manag., 13 (2020), 84. https://doi.org/10.3390/jrfm13040084 doi: 10.3390/jrfm13040084
|
| [35] |
G. N. Apostolakis, C. Floros, K. Gkillas, M. Wohar, Volatility spillovers across the spot and futures oil markets after news announcements, N. Am. J. Econ. Financ., 69 (2024), 102002. https://doi.org/10.1016/j.najef.2023.102002 doi: 10.1016/j.najef.2023.102002
|
| [36] |
L. R. Glosten, R. Jagannathan, D. E. Runkle, On the relation between the expected value and the volatility of the nominal excess return on stocks, J. Financ., 48 (1993), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x doi: 10.1111/j.1540-6261.1993.tb05128.x
|
| [37] |
A. S. S. Paiva, M. A. Rivera-Castro, R. F. S. Andrade, DCCA analysis of renewable and conventional energy prices, Physica A, 490 (2018), 1408–1414. https://doi.org/10.1016/j.physa.2017.08.052 doi: 10.1016/j.physa.2017.08.052
|
| [38] |
H. Y. Toda, T. Yamamoto, Statistical inference in vector autoregressions with possibly integrated processes, J. Econometrics, 66 (1995), 225–250. https://doi.org/10.1016/0304-4076(94)01616-8 doi: 10.1016/0304-4076(94)01616-8
|
| [39] |
M. Balcilar, S. Bekiros, R. Gupta, The role of news-based uncertainty indices in predicting oil markets: A hybrid nonparametric quantile causality method, Empir. Econ., 53 (2017), 879–889. https://doi.org/10.1007/s00181-016-1150-0 doi: 10.1007/s00181-016-1150-0
|
| [40] |
R. Koenker, G. Bassett Jr, Regression quantiles, Econometrica, 46 (1978), 33–50. https://doi.org/10.2307/1913643 doi: 10.2307/1913643
|
| [41] |
D. Lee, J. Baek, Stock prices of renewable energy firms: are there asymmetric responses to oil price changes? Economies, 6 (2018), 59. https://doi.org/10.3390/economies6040059 doi: 10.3390/economies6040059
|
| [42] | Y. Shin, B. Yu, M. Greenwood-Nimmo, Modelling asymmetric cointegration and dynamic multipliers in a non-linear ARDL framework, In: Festschrift in Honor of Peter Schmidt, New York: Springer, 2014,281–314. https://doi.org/10.1007/978-1-4899-8008-3_9 |
| [43] |
R. Ferrer, S. J. H. Shahzad, R. López, F. Jareño, Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Econ., 76 (2018), 1–20. https://doi.org/10.1016/j.eneco.2018.09.022 doi: 10.1016/j.eneco.2018.09.022
|
| [44] |
J. Baruník, T. Křehlík, Measuring the frequency dynamics of financial connectedness and systemic risk, J. Financ. Economet., 16 (2018), 271–296. https://doi.org/10.1093/jjfinec/nby001 doi: 10.1093/jjfinec/nby001
|
| [45] |
Z. Dai, H. Zhu, X. Zhang, Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle, Energy Econ., 109 (2022), 105959. https://doi.org/10.1016/j.eneco.2022.105959 doi: 10.1016/j.eneco.2022.105959
|
| [46] |
G. M. Caporale, N. Spagnolo, A. Almajali, Connectedness between fossil and renewable energy stock indices: The impact of the COP policies, Econ. Model., 123 (2023), 106273. https://doi.org/10.1016/j.econmod.2023.106273 doi: 10.1016/j.econmod.2023.106273
|
| [47] |
G. Koop, M. H. Pesaran, S. M. Potter, Impulse response analysis in non-linear multivariate models, J. Econometrics, 74 (1996), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4 doi: 10.1016/0304-4076(95)01753-4
|
| [48] |
H. H. Pesaran, Y. Shin, Generalized impulse response analysis in linear multivariate models, Econ. Lett., 58 (1998), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0 doi: 10.1016/S0165-1765(97)00214-0
|
| [49] |
L. Morales, B. Andreosso-O'Callaghan, The current global financial crisis: Do Asian stock markets show contagion or interdependence effects? J. Asian Econ., 23 (2012), 616–626. https://doi.org/10.1016/j.asieco.2012.09.002 doi: 10.1016/j.asieco.2012.09.002
|
| [50] |
M. Youssef, K. Mokni, A. N. Ajmi, Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter? Financ. Innov., 7 (2021), 13. https://doi.org/10.1186/s40854-021-00227-3 doi: 10.1186/s40854-021-00227-3
|
| [51] |
T. Liu, T. Nakajima, S. Hamori, The impact of economic uncertainty caused by COVID-19 on renewable energy stocks, Empir. Econ., 62 (2022), 1495–1515. https://doi.org/10.1007/s00181-021-02087-3 doi: 10.1007/s00181-021-02087-3
|
| [52] |
P. K. Narayan, Oil price news and COVID-19—Is there any connection? Energy Res. Lett., 1 (2020), 13176. https://doi.org/10.46557/001c.13176 doi: 10.46557/001c.13176
|
| [53] |
M. Akhtaruzzaman, S. Boubaker, M. Chiah, A. Zhong, COVID-19 and oil price risk exposure, Financ. Res. Lett., 42 (2021), 101882. https://doi.org/10.1016/j.frl.2020.101882 doi: 10.1016/j.frl.2020.101882
|
| [54] |
I. Chatziantoniou, D. Gabauer, R. Gupta, Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach, Resour. Policy, 84 (2023), 103729. https://doi.org/10.1016/j.resourpol.2023.103729 doi: 10.1016/j.resourpol.2023.103729
|
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Mohammed Alharbey, Turki Mohammed Alfahaid, Ousama Ben-Salha. Asymmetric volatility spillover between oil prices and regional renewable energy stock markets: A time-varying parameter vector autoregressive-based connectedness approach[J]. AIMS Mathematics, 2023, 8(12): 30639-30667. doi: 10.3934/math.20231566
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