Time-Varying Spillovers among Carbon, Oil, and Stock Market Volatility: Evidence from a TVP-VAR-DY Framework
DOI:
https://doi.org/10.62051/v15fhe94Keywords:
Carbon market, Crude oil, Stock market volatility, Good and bad volatility, TVP-VAR-DY, Time-varying spillovers.Abstract
Against the backdrop of global climate governance and increasing financial market integration, understanding the dynamic risk transmission among carbon, energy, and financial markets has become increasingly important. This paper investigates the time-varying volatility spillovers among the international carbon market, the crude oil market, and the Chinese and U.S. stock markets by distinguishing between good and bad volatility. Using monthly data from January 2008 to November 2024 and employing the TVP-VAR-DY connectedness framework, we examine the magnitude, direction, and evolution of volatility spillovers across markets. The empirical results show that the crude oil market consistently acts as a net volatility transmitter under both good and bad volatility systems, while the Chinese and U.S. stock markets mainly serve as net receivers. Moreover, spillovers associated with bad volatility are significantly stronger and more persistent than those associated with good volatility, highlighting the asymmetric nature of cross-market risk transmission. Pairwise connectedness analysis further indicates that the oil market exerts dominant spillover effects on both the carbon market and stock markets, whereas the carbon market transmits risk to the Chinese stock market during certain periods. These findings provide important implications for cross-market risk management, portfolio diversification, and policy coordination in the context of the global low-carbon transition.
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[1] Adekoya O B, Akinseye A B, Antonakakis N, et al. Crude oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies [J]. Resources Policy, 2022, 78: 102877.
[2] Akhtaruzzaman M, Boubaker S, Sensoy A. Financial contagion during COVID–19 crisis [J]. Finance research letters, 2021, 38: 101604.
[3] Alharbey M, Alfahaid T M, Ben-Salha O. 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.
[4] Ali F, Khurram M U. Leverage effects, volatility innovation spillovers, and inter-and intra-market asymmetric dependencies in cryptocurrencies and CFDs on equity indices: Evidence from high-frequency around-the-clock data [J]. International Review of Financial Analysis, 2025: 104636.
[5] Alomari M, Belghouthi H E, Mensi W, et al. Extreme time-frequency connectedness between energy sector markets and financial markets [J]. Economic Analysis and Policy, 2024, 84: 847-877.
[6] Andersen T G, Bollerslev T, Diebold F X, et al. Modeling and forecasting realized volatility [J]. Econometrica, 2003, 71 (2): 579-625.
[7] Andersen T G, Bollerslev T, Diebold F X, et al. The distribution of realized exchange rate volatility [J]. Journal of the American statistical association, 2001, 96 (453): 42-55.
[8] Antonakakis N, Chatziantoniou I, Gabauer D. Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions [J]. Journal of Risk and Financial Management, 2020, 13 (4): 84.
[9] Arouri M E H, Lahiani A, Nguyen D K. Return and volatility transmission between world oil prices and stock markets of the GCC countries [J]. Economic Modelling, 2011, 28 (4): 1815-1825.
[10] Avramov D, Chordia T, Goyal A. The impact of trades on daily volatility [J]. The Review of Financial Studies, 2006, 19 (4): 1241-1277.
[11] Baker S R, Bloom N, Davis S J. Measuring economic policy uncertainty [J]. The quarterly journal of economics, 2016, 131 (4): 1593-1636.
[12] Barndorff-Nielsen O E, Kinnebrock S, Shephard N. Measuring downside risk-realised semivariance [J]. CREATES Research Paper, 2008 (2008-42).
[13] Barndorff-Nielsen O E, Shephard N. Econometric analysis of realized volatility and its use in estimating stochastic volatility models [J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2002, 64 (2): 253-280.
[14] Barndorff-Nielsen O E, Shephard N. Power and bipower variation with stochastic volatility and jumps [J]. Journal of financial econometrics, 2004, 2 (1): 1-37.
[15] Baruník J, Kočenda E, Vácha L. Asymmetric volatility connectedness on the forex market [J]. Journal of International Money and Finance, 2017, 77: 39-56.
[16] Beckmann J, Czudaj R. Gold as an inflation hedge in a time-varying coefficient framework [J]. The North American Journal of Economics and Finance, 2013, 24: 208-222.
[17] Bekaert G, Wu G. Asymmetric volatility and risk in equity markets [J]. The review of financial studies, 2000, 13 (1): 1-42.
[18] BenSaïda A. Good and bad volatility spillovers: An asymmetric connectedness [J]. Journal of Financial Markets, 2019, 43: 78-95.
[19] Bloom N. The impact of uncertainty shocks [J]. econometrica, 2009, 77 (3): 623-685.
[20] Bollerslev T, Litvinova J, Tauchen G. Leverage and volatility feedback effects in high-frequency data [J]. Journal of Financial Econometrics, 2006, 4 (3): 353-384.
[21] Caldara D, Iacoviello M. Measuring geopolitical risk [J]. American economic review, 2022, 112 (4): 1194-1225.
[22] Chatziantoniou I, Gabauer D, Gupta R. Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach [J]. Resources Policy, 2023, 84: 103729.
[23] Chen X, Ghysels E. News—good or bad—and its impact on volatility predictions over multiple horizons [J]. The Review of Financial Studies, 2011, 24 (1): 46-81.
[24] Chen X, Yao Y, Wang L, et al. How EPU, VIX, and GPR interact with the dynamic connectedness among commodity and financial markets: Evidence from wavelet analysis [J]. The North American Journal of Economics and Finance, 2024, 74: 102217.
[25] Chevallier J. Macroeconomics, finance, commodities: Interactions with carbon markets in a data-rich model [J]. Economic modelling, 2011, 28 (1-2): 557-567.
[26] Corsi F. A simple approximate long-memory model of realized volatility [J]. Journal of financial econometrics, 2009, 7 (2): 174-196.
[27] Diebold F X, Yilmaz K. Better to give than to receive: Predictive directional measurement of volatility spillovers [J]. International Journal of forecasting, 2012, 28 (1): 57-66.
[28] Diebold F X, Yilmaz K. Measuring financial asset return and volatility spillovers, with application to global equity markets [J]. The Economic Journal, 2009, 119 (534): 158-171.
[29] Diebold F X, Yılmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms [J]. Journal of econometrics, 2014, 182 (1): 119-134.
[30] Ehrmann M, Fratzscher M, Rigobon R. Stocks, bonds, money markets and exchange rates: measuring international financial transmission [J]. Journal of Applied Econometrics, 2011, 26 (6): 948-974.
[31] Forbes K J, Rigobon R. No contagion, only interdependence: measuring stock market comovements [J]. The journal of Finance, 2002, 57 (5): 2223-2261.
[32] Forbes K J, Warnock F E. Capital flow waves—or ripples? Extreme capital flow movements since the crisis [J]. Journal of International Money and Finance, 2021, 116: 102394.
[33] Gavriilidis K. Measuring climate policy uncertainty [J]. Available at SSRN 3847388, 2021.
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