This paper investigates the systemic risk spillovers from the oil and gold markets to China financial market. Specifically, this paper uses wavelet analysis methods combined with time-varying copula models to calculate the CoVaR to explore the risk tail correlation and the strength of systemic risk spillover effects among oil, gold and China financial market under time-frequency conditions. The empirical results are as follows: First, there is a systemic risk spillover from the oil and gold markets to China financial market with significant time-varying characteristics. Second, changes in oil prices exacerbate risk spillovers from China financial markets, while price volatility in the gold market has a moderating effect on its risk spillovers, with system-wide risk spillovers increasing significantly in times of major economic crisis events. Third, oil market has a greater intensity of systemic risk spillover to China financial market than the gold market. External economic events significantly affect the intensity of risk spillovers from oil to China financial market, and abnormal volatility shocks in internal stock markets significantly affect the intensity of risk spillovers from gold to China financial market more significantly than the impact of external events. Furthermore, in the frequency domain, the oil and gold markets have some lagging effect on the transmission of systemic risks to the China financial market.
AlouiR.AïssaM. S. B. (2016). Relationship between oil, stock prices and exchange rates: A vine copula based GARCH method. The North American Journal of Economics and Finance, 37(7), 458–471. https://doi.org/10.1016/j.najef.2016.05.002
3.
AlouiR.AïssaM. S. B.NguyenD. K. (2011). Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure?Journal of Banking & Finance, 35(1), 130–141. https://doi.org/10.1016/j.jbankfin.2010.07.021
4.
ArouriE. H.LahianiA.NguyenD. K. (2015). Cross-market dynamics and optimal portfolio strategies in Latin American equity markets. European Business Review, 27(2), 16l–181. https://doi.org/10.1108/EBR-04-2013-0069
5.
BalcilarM.GuptaR.MillerS. M. (2015). Regime switching model of US crude oil and stock market prices: 1859 to 2013. Energy Economics, 49(5), 317–327. https://doi.org/10.1016/j.eneco.2015.01.026
6.
BasakS.PavlovaA. (2016). A model of financialization of commodities. The Journal of Finance, 71(4), 1511–1556. https://doi.org/10.1111/jofi.12408
BedouiR.BraiekS.GuesmiK.ChevallierJ. (2019). On the conditional dependence structure between oil, gold and USD exchange rates: Nested copula based GJR-GARCH model. Energy Economics, 80, 876–889. https://doi.org/10.1016/j.eneco.2019.02.002
9.
BekirosS.BoubakerS.NguyenD. K.UddinG. S. (2017). Black swan events and safe havens: The role of gold in globally integrated emerging markets. Journal of International Money and Finance, 73(B), 317–334. https://doi.org/10.1016/j.jimonfin.2017.02.010
10.
BetzF.HautschN.PeltonenT. A.SchienleM. (2016). Systemic risk spillovers in the European banking and sovereign network. Journal of Financial Stability, 25, 206–224. https://doi.org/10.1016/j.jfs.2015.10.006
11.
BoldanovR.DegiannakisS.FilisG. (2016). Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries. International Review of Financial Analysis, 48(12), 209–220. https://doi.org/10.1016/j.irfa.2016.10.002
12.
BollerslevT. (1987). A conditional Heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542–547. https://doi.org/10.2307/1925546
CaoJ.WenF.StanleyH. E.WangX. (2021). Multilayer financial networks and systemic importance: Evidence from China. International Review of Financial Analysis, 78, Article 101882. https://doi.org/10.1016/j.irfa.2021.101882
15.
CaoZ.ChenM.ZhaoL.YangG. (2025). Corporate site visits and the speed of leverage adjustment. Finance Research Letters, 73, Article 106638. https://doi.org/10.1016/j.frl.2024.106638
ChenL.WenF. H.LiW. Y.YinH.ZhaoL. L. (2022). Extreme risk spillover of the oil, exchange rate to Chinese stock market: Evidence from implied volatility indexes. Energy Economics, 107, Article 105857. https://doi.org/10.1016/j.eneco.2022.105857
18.
ChenX.ChenX.XuL.WenF. (2023). Attention to climate change and downside risk: Evidence from China. Risk Analysis: An Official Publication of the Society for Risk Analysis, 43(5), 1011–1031. https://doi.org/10.1111/risa.13975
19.
ChenX.WenF.XiaoJ., et al. (2025). Weathering the Risk: How Climate Uncertainty Fuels Corporate Fraud. Journal of Business Ethics, 201(2), 519–547. https://doi.org/10.1007/s10551-024-05857-y
20.
ChenX.HuangZ.LiZ.WenF. (2025a). Impact from a distance: Emotionally attached-place disasters and corporate risk-taking. Journal of Corporate Finance, 93, Article 102798. https://doi.org/10.1016/j.jcorpfin.2025.102798
21.
ChenY.ZhuH.LiuY. (2025b). Measuring multi-scale risk contagion between crude oil, clean energy, and stock market: A MODWT-Vine-copula method. Research in International Business and Finance, 75, Article 102790. https://doi.org/10.1016/j.ribaf.2025.102790
22.
ChkiliW. (2016). Dynamic correlations and hedging effectiveness between gold and stock markets: Evidence for BRIICS countries. Research in International Business and Finance, 38, 22–34. https://doi.org/10.1016/j.ribaf.2016.03.005
23.
ChoiK.HammoudehS. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy Policy, 38(8), 4388–4399. https://doi.org/10.1016/j.enpol.2010.03.067
24.
ClaytonD. G. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65(1), 141–151. https://doi.org/10.1093/biomet/65.1.141
DaiX.WangQ.ZhaD.ZhouD. (2020). Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach. Energy Economics, 88, Article 104774. https://doi.org/10.1016/j.eneco.2020.104774
27.
DaiZ.WuT. (2024). The impact of oil shocks on systemic risk of the commodity markets. Journal of Systems Science and Complexity, 37(6), 2697–2720. https://doi.org/10.1007/s11424-024-3224-y
28.
DaiZ.ZhuH.ChangX.WenF. (2025). Forecasting stock returns: The role of VIX-based upper and lower shadow of Japanese candlestick. Financial Innovation, 11(1), 19. https://doi.org/10.1186/s40854-024-00682-8
29.
DaiZ. F.ZhangX . (2023). Climate policy uncertainty and risks taken by the bank: Evidence from China. International Review of Financial Analysis, 87, Article 102579. https://doi.org/10.1016/j.irfa.2023.102579
30.
DaiZ. F.ZhuH. Y.ZhangX. (2022). Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle. Energy Economics, 109, Article 105959. https://doi.org/10.1016/j.eneco.2022.105959
31.
DemirerR.LeeH.-T.LienD. (2015). Does the stock market drive herd behavior in commodity futures markets?International Review of Financial Analysis, 39, 32–44. https://doi.org/10.1016/j.irfa.2015.02.006
Engle. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
34.
FarooqM.HammoudehS. (2007). Shock and volatility transmission in the oil,US and gulf equity markets. International Review of Economics and Finance, 16(3), 357–368. https://doi.org/10.1016/j.iref.2005.05.005
35.
JiQ.LiuB. Y.FanY. (2019). Risk dependence of CoVaR and structural change between oil prices and exchange rates: A time-varying copula model. Energy Economics, 77, 80–92. https://doi.org/10.1016/j.eneco.2018.07.012
36.
JiQ.LiuB. Y.NehlerH.UddinG. S. (2018). Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach. Energy Economics, 76, 115–126. https://doi.org/10.1016/j.eneco.2018.10.010
37.
JiangY.MuJ.NieH.WuL. (2020). Time-frequency analysis of risk spillovers from oil to BRICS stock markets: A long-memory Copula-CoVaR-MODWT method. International Journal of Finance & Economics, 27(3), 3386–3404. https://doi.org/10.1002/ijfe.2326
38.
KhalfaouiR.SolarinS. A.Al-QadasiA.Ben JabeurS. (2022). Dynamic causality interplay from COVID-19 pandemic to oil price, stock market, and economic policy uncertainty: Evidence from oil-importing and oil-exporting countries. Annals of Operations Research, 313(1), 1–39. https://doi.org/10.1007/s10479-021-04446-w
39.
KocaarslanB.SariR.SoytasU. (2017). Are there any diversification benefits among global finance center candidates in Eurasia?Emerging Markets Finance and Trade, 53(2), 357–374. https://doi.org/10.1080/1540496X.2016.1216838
40.
LiX.WeiY. (2018). The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method. Energy Economics, 74, 565–581. https://doi.org/10.1016/j.eneco.2018.07.011
41.
LiZ.WenF.HuangZ. J. (2023). Asymmetric response to earnings news across different sentiment states: The role of cognitive dissonance. Journal of Corporate Finance, 78, Article 102343. https://doi.org/10.1016/j.jcorpfin.2022.102343
42.
LiuB. Y.JiQ.FanY. (2017). A new time-varying optimal copula model identifying the dependence across markets. Quantitative Finance, 17(3), 437–453. https://doi.org/10.1080/14697688.2016.1205208
43.
LiuK.LuoC.LiZ. (2019). Investigating the risk spillover from crude oil market to BRICS stock markets based on copula-POT-CoVaR models. Quantitative Finance and Economics, 3(4), 754–771. https://doi.org/10.3934/QFE.2019.4.754
44.
LiuX.AnH.HuangS.WenS. (2017). The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH-BEKK model. Physica A: Statistical Mechanics and its Applications, 465, 374–383. https://doi.org/10.1016/j.physa.2016.08.043
45.
LiuY.WangJ.WenF.WuC. (2024). Climate policy uncertainty and bank systemic risk: A creative destruction perspective. Journal of Financial Stability, 73, Article 101289. https://doi.org/10.1016/j.jfs.2024.101289
46.
LonkaniR.ChangchitC.CutshallR.TreerotchananonA.PrasertsoontornT. (2025). The effect of investors’ emotional and depressive states on perceived returns and risk. Journal of Asian Business and Economic Studies, 32(3), 134–146. https://doi.org/10.1108/JABES-10-2024-0473
47.
MaF.LuX.ZhuB. (2023). Uncertainty and fluctuation in crude oil price: Evidence from machine learning models. Annals of Operations Research, 345(2-3), 725–755. https://doi.org/10.1007/s10479-023-05463-7
48.
MaY. R.ZhangD.JiQ.PanJ. (2019). Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?Energy Economics, 81, 536–544. https://doi.org/10.1016/j.eneco.2019.05.003
49.
MensiW.HammoudehcdS.ShahzaddeS.ShahbazdM. (2017). Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method. Journal of Banking & Finance, 75(2), 258–279. https://doi.org/10.1016/j.jbankfin.2016.11.017
50.
MensiW.HkiriB.Al-YahyaeeK. H.KangS. H. (2018). Analyzing time-frequency co-movements across gold and oil prices with BRICS stock markets: A VaR based on wavelet approach. International Review of Economics & Finance, 54, 74–102. https://doi.org/10.1016/j.iref.2017.07.032
51.
MensiW.YousafI.VoX. V.KangS. H. (2022). Asymmetric spillover and network connectedness between gold, BRENT oil and EU subsector markets. Journal of International Financial Markets, Institutions and Money, 76, Article 101487. https://doi.org/10.1016/j.intfin.2021.101487
52.
MiaoS.TianG. G.WenF.XiaoJ. (2024a). The Independence of judges and corporate social responsibility. Journal of Business Ethics, 193(3), 633–653. https://doi.org/10.1007/s10551-023-05540-8
ReboredoJ. C.Rivera-CastroM. A.UgoliniA. (2016). Downside and upside risk spillovers between exchange rates and stock prices. Journal of Banking & Finance, 62, 76–96. https://doi.org/10.1016/j.jbankfin.2015.10.011
59.
TekinH.YavuzP. (2025). Does corporate sustainability matter for the capital structure puzzle in OIC countries? Evidence from the COVID-19 pandemic. Journal of Asian Business and Economic Studies, 32(1), 2–14. https://doi.org/10.1108/JABES-01-2024-0029
60.
TiwariA. K.MutascuM. I.AlbulescuC. T. (2013). The influence of the international oil prices on the real effective exchange rate in Romania in a wavelet transform frame work. Energy Economics, 40(40), 714–733. https://doi.org/10.1016/j.eneco.2013.08.016
61.
WalidM. S.BesmaH.KhamisH.AlY. Y.SangH. K. (2018). Analyzing time-frequency Co-movements across gold and oil prices with BRICS stock markets: A VaR based on wavelet approach. International Review of Economics and Finance, 54, 74–102. https://doi.org/10.1016/j.iref.2017.07.032
WenF.LiuZ.DaiZ.HeS.LiuW. (2022). Multi-scale risk contagion among international oil market, Chinese commodity market and Chinese stock market: A MODWT-vine quantile regression approach. Energy Economics, 109, Article 105957. https://doi.org/10.1016/j.eneco.2022.105957
64.
WenF.ZhaoL.HeS.YangG. (2020). Asymmetric relationship between carbon emission trading market and stock market: Evidences from China. Energy Economics, 91, Article 104850. https://doi.org/10.1016/j.eneco.2020.104850
65.
WenF.WengK.ZhouW. X. (2020). Measuring the contribution of Chinese financial institutions to systemic risk: An extended asymmetric CoVaR approach. Risk Management, 22(4), 310–337. https://doi.org/10.1057/s41283-020-00064-1
XiaoJ.HuC.OuyangG.WenF. (2019). Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach. Energy Economics, 80, 297–309. https://doi.org/10.1016/j.eneco.2019.01.016
68.
YangL. S.ZhuY. M.WangY. D.WangY. Q. (2016). Multifractal Detrended Cross--correlations between crude oil market and Chinese ten sector stock markets. Physica A: Statistical Mechanics and its Applications, 462, 255–265. https://doi.org/10.1016/j.physa.2016.06.040
69.
YangX.PuK. X.ChenL.ShiL. (2025). Climate risk and corporate maturity mismatch: International evidence. Applied Economics, Advanced Online Publication. https://doi.org/10.1080/00036846.2025.2500700
70.
ZhangY.LiuY.TangY.GaoQ. (2024). Large shareholders’ stock selling and corporate performance: Evidence from China. Pacific-Basin Finance Journal, 86(9), Article 102426. https://doi.org/10.1016/j.pacfin.2024.102426
71.
ZhouJ. (2017). Multiscale analysis of international linkages of REIT returns and volatilities. The Journal of Real Estate Finance and Economics, 45(4), 1062–1087. https://doi.org/10.1007/s11146-011-9302-7
