Examining the interactions of carbon, electricity, and natural gas markets

Abstract

This paper analyzes the relationship between the carbon, electricity and natural gas markets in Europe. To identify the origin and paths of price transmission among these markets, we employ the Diebold Yilmaz spillover approach. To investigate the multiscale response to price signals, we use the time-varying parameter stochastic volatility vector autoregression system. The results provide evidence that the market for natural gas plays a significant role in setting carbon prices, which are negative in the short-term and positive in the medium term. These effects can, however, be negated by the Russia–Ukraine conflict, and the resulting market for natural gas does not exert any such shocks on the electricity market. Since the conflict, the electricity market has become a major price transmitter and has produced short-term positive but medium-term negative effects on the carbon market. Our results suggest that short- and medium-term policies should focus on avoiding price distortions and stabilizing markets.

Graphical abstract

Keywords

carbon market / natural gas market / electricity market / response mechanism / price transmission

References

[1] Andrianesis P, Biskas P, Liberopoulos G, (2021). Evaluating the cost of emissions in a pool-based electricity market. Applied Energy, 298: 117253

[2] 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

[3] Batten J A, Maddox G E, Young M R, (2021). Does weather, or energy prices, affect carbon prices. Energy Economics, 96: 105016

[4] Baur D G, McDermott T K, (2010). Is gold a safe haven? International evidence. Journal of Banking & Finance, 34( 8): 1886–1898

[5] BP (2022). BP Statistical Review of World Energy 2022. Available at the website of BP

[6] BruninxKOvaere M (2021). Waterbed leakage drives EU ETS emissions: COVID-19, the Green Deal & the recovery plan. Preprint. DOI: 10.21203/rs.3.rs-270917/v1

[7] Bruninx K, Ovaere M, Delarue E, (2020). The long-term impact of the market stability reserve on the EU emission trading system. Energy Economics, 89: 104746

[8] Chen Y, Wei Y, Bai L, Zhang J, (2023). Can Green Economy stocks hedge natural gas market risk? Evidence during Russia-Ukraine conflict and other crisis periods. Finance Research Letters, 53: 103632

[9] Chen Z, Zhao S, Song H, Yang M, Li S, (2024). Dynamic volatility spillover relationships between the Chinese carbon and international energy markets from extreme climate shocks. International Review of Economics & Finance, 92: 626–645

[10] Chevallier J, Khuong Nguyen D, Carlos Reboredo J, (2019). A conditional dependence approach to CO₂-energy price relationships. Energy Economics, 81: 812–821

[11] Chuliá H, Furio D, Uribe J M, (2019). Volatility spillovers in energy markets. Energy Journal, 40( 3): 173–198

[12] Csereklyei Z, Stern D I, (2018). Technology choices in the US electricity industry before and after market restructuring. Energy Journal, 39( 5): 157–182

[13] Dai X, Xiao L, Wang Q, Dhesi G, (2021). Multiscale interplay of higher-order moments between the carbon and energy markets during Phase III of the EU ETS. Energy Policy, 156: 112428

[14] Diebold F X, Yilmaz K, (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119( 534): 158–171

[15] Diebold F X, Yilmaz K, (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28( 1): 57–66

[16] Diebold F X, Yilmaz K, (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182( 1): 119–134

[17] Duan K, Ren X, Shi Y, Mishra T, Yan C, (2021). The marginal impacts of energy prices on carbon price variations: Evidence from a quantile-on-quantile approach. Energy Economics, 95: 105131

[18] Fang Y, Shao Z, (2022). The Russia–Ukraine conflict and volatility risk of commodity markets. Finance Research Letters, 50: 103264

[19] Fuller D W A, (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 79( 366): 355–367

[20] Gong X, Shi R, Xu J, Lin B, (2021). Analyzing spillover effects between carbon and fossil energy markets from a time-varying perspective. Applied Energy, 285: 116384

[21] Gong X, Zhao M, Wu Z, Jia K, Xiong X, (2023). Research on tail risk contagion in international energy markets—The quantile time-frequency volatility spillover perspective. Energy Economics, 121: 106678

[22] Ha L T, Bouteska A, Sharif T, Abedin M Z, (2024). Dynamic interlinkages between carbon risk and volatility of green and renewable energy: A TVP-VAR analysis. Research in International Business and Finance, 69: 102278

[23] Jiang W, Dong L, Liu X, (2023). How does COVID-19 affect the spillover effects of green finance, carbon markets, and renewable/non-renewable energy markets? Evidence from China. Energy, 281: 128351

[24] Koop G, Korobilis D, (2014). A new index of financial conditions. European Economic Review, 71: 101–116

[25] Koop G, Pesaran M H, Potter S M, (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74( 1): 119–147

[26] Le Quéré C, Jackson R B, Jones M W, Smith A J P, Abernethy S, Andrew R M, De-Gol A J, Willis D R, Shan Y, Canadell J G, Friedlingstein P, Creutzig F, Peters G P, (2020). Temporary reduction in daily global CO₂ emissions during the COVID-19 forced confinement. Nature Climate Change, 10( 7): 647–653

[27] Lin B, Xu B, (2021). A non-parametric analysis of the driving factors of China’s carbon prices. Energy Economics, 104: 105684

[28] Lin B Q, Jia Z J, (2019). What are the main factors affecting carbon price in Emission Trading Scheme? A case study in China. Science of the Total Environment, 654: 525–534

[29] Man Y, Zhang S, He Y, (2024). Dynamic risk spillover and hedging efficacy of China’s carbon-energy-finance markets: Economic policy uncertainty and investor sentiment non-linear causal effects. International Review of Economics & Finance, 93: 1397–1416

[30] Meyer P B, Schwarze R, (2019). Financing climate-resilient infrastructure: Determining risk, reward, and return on investment. Frontiers of Engineering Management, 6( 1): 117–127

[31] Mosquera-López S, Nursimulu A, (2019). Drivers of electricity price dynamics: Comparative analysis of spot and futures markets. Energy Policy, 126: 76–87

[32] Nakajima J, (2011). Time-varying parameter VAR model with stochastic volatility: an overview of methodology and empirical applications. Monetary and Economic Studies, 29: 107–142

[33] Pesaran H H, Shin Y, (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58( 1): 17–29

[34] Primiceri G E, (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72( 3): 821–852

[35] Qiao S, Guo Z X, Tao Z, Ren Z Y, (2023). Analyzing the network structure of risk transmission among renewable, non-renewable energy and carbon markets. Renewable Energy, 209: 206–217

[36] Tan X, Sirichand K, Vivian A, Wang X, (2020). How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics. Energy Economics, 90: 104870

[37] Tan X, Wang X, (2017). Dependence changes between the carbon price and its fundamentals: A quantile regression approach. Applied Energy, 190: 306–325

[38] Uribe J M, Mosquera-López S, Arenas O J, (2022). Assessing the relationship between electricity and natural gas prices in European markets in times of distress. Energy Policy, 166: 113018

[39] Vellachami S, Hasanov A S, Brooks R, (2023). Risk transmission from the energy markets to the carbon market: Evidence from the recursive window approach. International Review of Financial Analysis, 89: 102715

[40] Wang H, Yang Y, (2023). Decomposition analysis applied to energy and emissions: A literature review. Frontiers of Engineering Management, 10( 4): 625–639

[41] Wang K, Wang Z, Yunis M, Kchouri B, (2023). Spillovers and connectedness among climate policy uncertainty, energy, green bond and carbon markets: A global perspective. Energy Economics, 128: 107170

[42] Wang M, Zhou P, (2020). Impact of permit allocation on cap-and-trade system performance under market power. Energy Journal, 41( 6): 215–232

[43] Wang M, Zhou P, (2022). A two-step auction-refund allocation rule of CO₂ emission permits. Energy Economics, 113: 106179

[44] Wu F, Wang S Y, Zhou P, (2023a). Marginal abatement cost of carbon dioxide emissions: The role of abatement options. European Journal of Operational Research, 310( 2): 891–901

[45] Wu R, Qin Z, Liu B, (2023b). Connectedness between carbon and sectoral commodity markets: Evidence from China. Research in International Business and Finance, 66: 102073

[46] Yang Y, Wang H, Loschel A, Zhou P, (2022). Energy transition toward carbon-neutrality in China: Pathways, implications and uncertainties. Frontiers of Engineering Management, 9( 3): 358–372

[47] Zhou P, Gao S, Lv Y, Zhao G, (2022). Energy transition management towards a low-carbon world. Frontiers of Engineering Management, 9( 3): 499–503

[48] Zhou P, Lv Y, Wen W, (2023). The low-carbon transition of energy systems: A bibliometric review from an engineering management perspective. Engineering, 29: 147–158

[49] Zhu B, Ye S, He K, Chevallier J, Xie R, (2019). Measuring the risk of European carbon market: An empirical mode decomposition-based value at risk approach. Annals of Operations Research, 281( 1–2): 373–395

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