This article explores the time-varying connectedness of systemic risk and return of new energy vehicle firms, oil prices and U.S.-China tensions (UCT), which adopts Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) based R2 decomposed connectedness method. The main findings are as follows: (1) In return layer, average total connectedness index (TCI) is 55.87%; in systemic risk layer, the TCI is 94.17%. These both indicate a high degree of interconnectedness. (2) TCI is time-varying and the trend is affected by major external shocks. (3) UCT and oil prices are the senders of shocks in return layer and the receivers of shocks in systemic risk layer. The paper plot multi-networks in order to visualize the transmission paths and intensity. New energy vehicle enterprises should pay attention to the international political and economic situation, in order to reduce systemic risk and help stabilize the financial market.

Xiaotong Zhang. Time-Varying Connectedness among New Energy Vehicle Firms, Oil and U.S.-China Tensions: Evidence from Return and Systemic Risk. Academic Journal of Business & Management (2025), Vol. 7, Issue 7: 10-17. https://doi.org/10.25236/AJBM.2025.070702.

[1] Cheng Z, Li M, Cui R, et al. The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis[J]. International Review of Financial Analysis, 2024, 95: 103454.

[2] Deng X, Xu F. Connectedness between international oil and China's new energy industry chain: A time-frequency analysis based on TVP-VAR model[J]. Energy Economics, 2024, 140: 107954.

[3] Zhou Y, Wang D, Nie Z. How geopolitical tensions affect China’s systemic financial risk contagion[J]. China Economic Review, 2025, 90: 102366.

[4] Nadeem N, Jadoon I A, Aslam F, et al. Return connectedness and portfolio implications of green equities: A comparison of green and conventional investment modes[J]. Journal of Environmental Management, 2025, 384: 125647.

[5] Cai Y, Zhang Y, Zhang A. Oil price shocks and airlines stock return and volatility–A GFEVD analysis[J]. Economics of Transportation, 2025, 41: 100396.

[6] Ouyang Z, Liu M, Huang S, et al. Does the source of oil price shocks matter for the systemic risk?[J]. Energy Economics, 2022, 109: 105958.

[7] Acharya V V, Pedersen L H, Philippon T, et al. Measuring systemic risk[J]. The review of financial studies, 2017, 30(1): 2-47.

[8] Cocca T, Gabauer D, Pomberger S. Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures[J]. Energy Economics, 2024, 136: 107680.

[9] 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.

[10] 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.

[11] D'Agostino R B. Transformation to normality of the null distribution of g 1[J]. Biometrika, 1970: 679-681.

[12] Anscombe F J, Glynn W J. Distribution of the kurtosis statistic b 2 for normal samples[J]. Biometrika, 1983, 70(1): 227-234.

[13] Jarque C M, Bera A K. Efficient tests for normality, homoscedasticity and serial independence of regression residuals[J]. Economics letters, 1980, 6(3): 255-259.

[14] Elliott G, Rothenberg T G, Stock J H. Efficient tests for an autoregressive unit root[J]. Econometrica, 1996, 64(4): 813-836.

[15] Fisher T J, Gallagher C M. New weighted portmanteau statistics for time series goodness of fit testing[J]. Journal of the American Statistical Association, 2012, 107(498): 777-787.

[16] Engle R. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models[J]. Journal of business & economic statistics, 2002, 20(3): 339-350.

[17] Antonakakis N, Chatziantoniou I, Gabauer D. The impact of Euro through time: Exchange rate dynamics under different regimes[J]. International Journal of Finance & Economics, 2021, 26(1): 1375-1408.

[18] Dai Z, Zhang X. Climate policy uncertainty and risks taken by the bank: evidence from China[J]. International Review of Financial Analysis, 2023, 87: 102579.

[19] Shi Y, Feng Y, Zhang Q, et al. Does China's new energy vehicles supply chain stock market have risk spillovers? Evidence from raw material price effect on lithium batteries[J]. Energy, 2023, 262: 125420.