Abstract
This paper uses a two-regime Markov-switching GARCH model to illustrate that the state-switching of returns in the EU carbon market and its associated energy and stock markets is related to the occurrence of major events. Using the European economic policy uncertainty (EEPU) index as the proxy for the turbulence in the economic policies caused by major events, the Granger causality test shows that EEPU is associated with the three markets. To quantitatively analyze the impact of EEPU on the three markets, the GJRGARCH(1,1)-MIDAS model is constructed to solve the mixed frequency problem. The results indicate that EEPU has a negative effect on the long-term volatility of carbon and energy markets, but a positive effect on the stock market. The forecasting performance of the mixed frequency model considering EEPU is better than that of traditional GARCH models. Based on the decomposition of volatility in the mixed frequency model, the spillover index model is applied to analyze the spillover effects of the "carbon-energy-finance" system at three levels: total volatility, short-term volatility, and long-term volatility. The results indicate that at the level of long-term volatility affected by EEPU, the spillover effect of the system fluctuates more frequently and with greater intensity. The energy and stock markets play different roles at different levels of volatility, but the carbon market is always a risk receiver. The pairwise net spillover effects of the energy and stock markets on the carbon market are time-varying and related to the occurrence of major events.
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Data availability
The EEPU index data comes from the economic policy uncertainty website (http://www.PolicyUncertainty.com), and other data comes from the Wind database.
References
Adediran IA, Swaray R (2023) Carbon trading amidst global uncertainty: the role of policy and geopolitical uncertainty. Econ Model 123:106279. https://doi.org/10.1016/j.econmod.2023.106279
Antonakakis N, Chatziantoniou I, 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
Bairagi RK (2022) Dynamic impacts of economic policy uncertainty on Australian stock market: an intercontinental evidence. Journal of Emerging Market Finance 21(1):64–91. https://doi.org/10.1177/097265272110696
Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Q J Econ 131(4):1593–1636. https://doi.org/10.1093/qje/qjw024
Balcilar M, Demirer R, Hammoudeh S, Nguyen DK (2016) Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk. Energy Economics 54:159–172. https://doi.org/10.1016/j.eneco.2015.11.003
Berta N, Gautherat E, Gun O (2017) Transactions in the European carbon market: a bubble of compliance in a whirlpool of speculation. Camb J Econ 41(2):575–593. https://doi.org/10.1093/cje/bew041
Boubaker S, Goodell JW, Pandey DK, Kumari V (2022) Heterogeneous impacts of wars on global equity markets: evidence from the invasion of Ukraine. Financ Res Lett 48:102934. https://doi.org/10.1016/j.frl.2022.102934
Cao GX, Xie F (2023) Extreme risk spillovers across energy and carbon markets: Evidence from the quantile extended joint connectedness approach. International Journal of Finance and Economics 2023. https://doi.org/10.1002/ijfe.2781
Cao GX, Xie F, Ling MJ (2022) Spillover effects in Chinese carbon, energy and financial markets. International Finance 25(3):416–434. https://doi.org/10.1111/infi.12417
Celık S (2012) The more contagion effect on emerging markets: the evidence of DCC-GARCH model. Econ Model 29(5):1946–1959. https://doi.org/10.1016/j.econmod.2012.06.011
Chang K, Ye ZF, Wang WH (2019) Volatility spillover effect and dynamic correlation between regional emissions allowances and fossil energy markets: new evidence from China’s emissions trading scheme pilots. Energy 185:1314–1324. https://doi.org/10.1016/j.energy.2019.07.132
Chen W, Wei Y, Lang QQ, Lin Y, Liu MJ (2014) Financial market volatility and contagion effect: a copula-multifractal volatility approach. Physica A 398:289–300. https://doi.org/10.1016/j.physa.2013.12.016
Chen ZL, Zhang L, Weng C (2023) Does climate policy uncertainty affect Chinese stock market volatility. Int Rev Econ Financ 84:369–381. https://doi.org/10.1016/j.iref.2022.11.030
Christou C, Gupta R (2020) Forecasting equity premium in a panel of OECD countries: The role of economic policy uncertainty. Q Rev Econ Finance 76:243–248. https://doi.org/10.1016/j.qref.2019.08.001
Cifter A (2013) Forecasting electricity price volatility with the Markov-switchingGARCH model: evidence from the Nordic electric power market. Electric Power Systems Research 102:61–67. https://doi.org/10.1016/j.epsr.2013.04.007
Conrad C, Kleen O (2020) Two are better than one: volatility forecasting using multiplicative component GARCH-MIDAS models. Applied Econometrics 35(1):19–45. https://doi.org/10.1002/jae.2742
Dai ZF, Zhu HY (2023) Dynamic risk spillover among crude oil, economic policy uncertainty and Chinese financial sectors. Int Rev Econ Financ 83:421–450. https://doi.org/10.1016/j.iref.2022.09.005
Dai PF, Xiong X, Huynh TLD, Wang JQ (2022) The impact of economic policy uncertainties on the volatility of European carbon market. J Commod Mark 26:100208. https://doi.org/10.1016/j.jcomm.2021.100208
Dibiasi A, Abberger K, Siegenthaler M, Sturm JE (2018) The effects of policy uncertainty on investment: evidence from the unexpected acceptance of a far-reaching referendum in Switzerland. Eur Econ Rev 104:38–67. https://doi.org/10.1016/j.euroecorev.2018.01.002
Diebold FX, Yılmaz K (2009) Measuring financial asset return and volatility spillovers, with application to global equity markets. Econ J 119(534):158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
Diebold FX, Yılmaz K (2012) Better to give than to receive: predictive directional measurement of volatility spillovers. Int J Forecast 28(1):57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
Ding Q, Huang JB, Zhang HW (2022) Time-frequency spillovers among carbon, fossil energy and clean energy markets: The effects of attention to climate change. Int Rev Financ Anal 83:102222.https://doi.org/10.1016/j.irfa.2022.102222
Dou Y, Li YY, Dong KY, Ren XH (2022) Dynamic linkages between economic policy uncertainty and the carbon futures market: does Covid-19 pandemic matter? Resour Policy 75:102455. https://doi.org/10.1016/j.resourpol.2021.102455
Fan Y, Jia JJ, Wang X, Xu JH (2017) What policy adjustments in the EU ETS truly affected the carbon prices? Energy Policy 103:145–164. https://doi.org/10.1016/j.enpol.2017.01.008
Gao RZ, Zhao YC, Zhang B (2021) The spillover effects of economic policy uncertainty on the oil, gold, and stock markets: evidence from China. Int J Financ Econ 26(2):2134–2141. https://doi.org/10.1002/ijfe.1898
Gao FW, Wu YM, Chen D, Hu MY (2023) The spillover effect among CET market, coal market, and new energy market for dual-carbon target: New evidence from China. Discrete Dyn Nat Soc 5126128. https://doi.org/10.1155/2023/5126128
Ghadge A, van der Werf S, Er Kara M, Goswami M, Kumar P, Bourlakis M (2020) Modelling the impact of climate change risk on bioethanol supply chains. Technol Forecast Soc Chang 160:120227. https://doi.org/10.1016/j.techfore.2020.120227
Guo XZ, Wang Y, Hao YX, Zhang WW (2023) Spillover effect among carbon bond market, carbon stock market and energy stock market: evidence from China. Financ Res Lett 58:104521. https://doi.org/10.1016/j.frl.2023.104521
Hailemariam A, Smyth R, Zhang XB (2019) Oil prices and economic policy uncertainty: evidence from a nonparametric panel data model. Energy Econ 83:40–51. https://doi.org/10.1016/j.eneco.2019.06.010
Han M, Ding LL, Zhao X, Kang WL (2019) Forecasting carbon prices in the Shenzhen market, China: the role of mixed-frequency factors. Energy 171:69–76. https://doi.org/10.1016/j.energy.2019.01.009
Hansen PR, Lunde A, Nason JM (2011) The model confidence set. Econometrica 79(2):453–497. https://doi.org/10.3982/ECTA5771
He F, Wang ZW, Yin LB (2020) Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock market. N Am J Econ Finance 51:101084. https://doi.org/10.1016/j.najef.2019.101084
Ji Q, Zhang DY, Geng JB (2018) Information linkage, dynamic spillovers in prices and volatility between the carbon and energy markets. J Clean Prod 198:972–978. https://doi.org/10.1016/j.jclepro.2018.07.126
W Jiang YF Chen 2022 The time-frequency connectedness among metal, energy and carbon markets pre and during COVID-19 outbreak Resour Policy 77 102763https://doi.org/10.1016/j.resourpol.2022.102763
Jiang SR, Li YZ, Lu QY, Wang SY, Wei YJ (2022) Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets. Res Int Bus Financ 59:101543. https://doi.org/10.1016/j.ribaf.2021.101543
Jiménez-Rodríguez R (2019) What happens to the relationship between EU allowances prices and stock market indices in Europe? Energy Economics 81:13–24. https://doi.org/10.1016/j.eneco.2019.03.002
Jin HF, Jin L (2008) The spillover between stock market and international oil market: the comparative analysis on China and USA. J Financ Res 2:83–97
King D, Botha F (2015) Modelling stock return volatility dynamics in selected African markets. Econ Model 45:50–73. https://doi.org/10.1016/j.econmod.2014.11.008
Li CY, Chen SN, Lin SK (2016) Pricing derivatives with modeling CO2 emission allowance using a regime-switching jump diffusion model: with regime-switching risk premium. The European Journal of Finance 22(10):887–908. https://doi.org/10.1080/1351847X.2015.1050526
Li R, Li SF, Yuan D, Chen H, Xiang SL (2023) Spillover effect of economic policy uncertainty on the stock market in the post-epidemic era. The North American Journal of Economics and Finance 64:101846. https://doi.org/10.1016/j.najef.2022.101846
Lin BQ, Bai R (2021) Oil prices and economic policy uncertainty: Evidence from global, oil importers, and exporters’ perspective. Res Int Bus Financ 56:101357. https://doi.org/10.1016/j.ribaf.2020.101357
Lin BQ, Chen YF (2019) Dynamic linkages and spillover effects between CET market, coal market and stock market of new energy companies: a case of Beijing CET market in China. Energy 172:1198–1210. https://doi.org/10.1016/j.energy.2019.02.029
Liu HH, Chen YC (2013) A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: the impacts of extreme weather. Econ Model 35:840–855. https://doi.org/10.1016/j.econmod.2013.08.007
Liu TY, Gong X (2020) Analyzing time-varying volatility spillovers between the crude oil markets using a new method. Energy Econ 87:104711. https://doi.org/10.1016/j.eneco.2020.104711
Liu LP, Lü Z (2023) Policy uncertainty, geopolitical risks and China’s carbon neutralization. Carbon Management 14(1):2251929. https://doi.org/10.1080/17583004.2023.2251929
Liu J, Zhang ZT, Yan LZ, Wen FH (2021) Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model. Financial Innovation 7(1):76.https://doi.org/10.1186/s40854-021-00292-8
Liu J, Hu Y, Yan LZ, Chang CP (2023a) Volatility spillover and hedging strategies between the European carbon emissions and energy markets. Energ Strat Rev 46:101058. https://doi.org/10.1016/j.esr.2023.101058
Liu T, Guan XY, Wei YG, Xue S, Xu L (2023b) Impact of economic policy uncertainty on the volatility of China’s emission trading scheme pilots. Energy Econ 212:106626. https://doi.org/10.1016/j.eneco.2023.106626
Ma Y, Wang LH, Zhang T (2020) Research on the dynamic linkage among the carbon emission trading, energy and capital markets. J Clean Prod 272:122717. https://doi.org/10.1016/j.jclepro.2020.122717
Martin R, Muûls M, de Preux LB, Wagner UJ (2014) On the empirical content of carbon leakage criteria in the EU Emissions Trading Scheme. Ecol Econ 105:78–88. https://doi.org/10.1016/j.ecolecon.2014.05.010
Osah TT, Mollick AV (2023) Stock and oil price returns in international markets: Identifying short and long-run effects. J Econ Financ 47:116–141. https://doi.org/10.1007/s12197-022-09602-x
Pástor L, Veronesi P (2012) Uncertainty about government policy and stock prices. J Financ 67(4):1219–1264. https://doi.org/10.1111/j.1540-6261.2012.01746.x
Qiao S, Zhao CX, Zhang KQ, Ren ZY (2021) Research on time-varying two-way spillover effects between carbon and energy markets: empirical evidence from China. Frontiers in Energy Research 9:789871. https://doi.org/10.3389/fenrg.2021.789871
Ren XH, Dou Y, Dong KY, Yan C (2023) Spillover effects among crude oil, carbon, and stock markets: evidence from nonparametric causality-in-quantiles tests. Appl Econ 55(38):4486–4509. https://doi.org/10.1080/00036846.2022.2128297
Sánchez GJ, Cruz RS (2023) Volatility spillovers between oil and financial markets during economic and financial crises: a dynamic approach. Journal of Economics and Finance 47:1018–1040. https://doi.org/10.1007/s12197-023-09634-x
Sarker PK, Bouri E, Marco CKL (2023) Asymmetric effects of climate policy uncertainty, geopolitical risk, and crude oil prices on clean energy prices. Environ Sci Pollut Res 30:15797–15807. https://doi.org/10.1007/s11356-022-23020-w
Su Z, Fang T, Yin L (2019) Understanding stock market volatility: what is the role of US uncertainty? The North American Journal of Economics and Finance 48:582–590. https://doi.org/10.1016/j.najef.2018.07.014
Sweidan OD (2021) The geopolitical risk effect on the US renewable energy deployment. J Clean Prod 293:126189. https://doi.org/10.1016/j.jclepro.2021.126189
Syed QR, Bouri E (2022a) Spillovers from global economic policy uncertainty and oil price volatility to the volatility of stock markets of oil importers and exporters. Environ Sci Pollut Res 29:15603–15613. https://doi.org/10.1007/s11356-021-16722-0
Syed QR, Bouri E (2022b) Impact of economic policy uncertainty on CO2 emissions in the US: evidence from bootstrap ARDL approach. J Public Aff 22(3):e2595. https://doi.org/10.1002/pa.2595
Tan XP, Sirichand K, Vivian A, Wang XY (2020) How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics. Energy Economics 90:104870. https://doi.org/10.1016/j.eneco.2020.104870
Wang YD, Guo ZY (2018) The dynamic spillover between carbon and energy markets: new evidence. Energy 149:24–33. https://doi.org/10.1016/j.energy.2018.01.145
Wang L, Ma F, Liu J, Yang L (2020) Forecasting stock price volatility: new evidence from the GARCH-MIDAS model. Int J Forecast 36(2):684–694. https://doi.org/10.1016/j.ijforecast.2019.08.005
Wang X, Li JY, Ren XH (2022) Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond. Int Rev Financ Anal 83:102306. https://doi.org/10.1016/j.irfa.2022.102306
Wang X, Li JY, Ren XH, Bu RJ, Jawadi F (2023) Economic policy uncertainty and dynamic correlations in energy markets: assessment and solutions. Energy Economics 117:106475. https://doi.org/10.1016/j.eneco.2022.106475
Wen FH, Wu N, Gong X (2020) China’s carbon emissions trading and stock returns. Energy Econ 86:104627. https://doi.org/10.1016/j.eneco.2019.104627
Wu XY, Jiang ZT (2023) Time-varying asymmetric volatility spillovers among China’s carbon markets, new energy market and stock market under the shocks of major events. Energy Economics 126:107004. https://doi.org/10.1016/j.eneco.2023.107004
Xia ML, Chen ZH, Wang P (2023) Dynamic risk spillover effect between the carbon and stock markets under the shocks from exogenous events. Energies 16(1):97. https://doi.org/10.3390/en16010097
Yang L, Hamori S (2021) Systemic risk and economic policy uncertainty: international evidence from the crude oil market. Econ Anal Policy 69:142–158. https://doi.org/10.1016/j.eap.2020.12.001
Yao Y, Tian LX, Cao GX (2022) The information spillover among the carbon market, energy market, and stock market: a case study of China’s pilot carbon markets. Sustainability 14(8):4479. https://doi.org/10.3390/su14084479
Zaremba A, Cakici N, Demir E, Long H (2022) When bad news is good news: geopolitical risk and the cross-section of emerging market stock returns. J Financ Stab 58:100964. https://doi.org/10.1016/j.jfs.2021.100964
Zeng Q, Ma F, Lu XJ, Xu WJ (2022) Policy uncertainty and carbon neutrality: evidence from China. Financ Res Lett 47:102771. https://doi.org/10.1016/j.frl.2022.102771
Zhang YJ, Sun YF (2016) The dynamic volatility spillover between European carbon trading market and fossil energy market. J Clean Prod 112:2654–2663. https://doi.org/10.1016/j.jclepro.2015.09.118
Zhang JR, Hassan K, Wu ZC, Gasbarro D (2022) Does corporate social responsibility affect risk spillovers between the carbon emissions trading market and the stock market? J Clean Prod 362:132330. https://doi.org/10.1016/j.jclepro.2022.132330
Zhang H, Gong ZT, Yang YL, Chen F (2023) Dynamic connectedness between China green bond, carbon market and traditional financial markets: evidence from quantile connectedness approach. Financ Res Lett 58:104473. https://doi.org/10.1016/j.frl.2023.104473
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This work is supported by the National Social Science Foundation of China (23BJY088).
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Liu, Y., Yan, B. Spillover effects of carbon, energy, and stock markets considering economic policy uncertainty. J Econ Finan (2024). https://doi.org/10.1007/s12197-024-09665-y
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DOI: https://doi.org/10.1007/s12197-024-09665-y