Geopolitical threats have long been a cause of concern for the global energy market. The increase in oil prices and related economic impacts have caused wariness on the part of stockholders due to the unpredictability of impacts. It is against this background that the current paper examines symmetric spillovers of oil to 12 Asia-Pacific stocks through M-GARCH and wavelet coherence analysis. It attempts to unveil the short-term and long-term behaviour and shifts in developed markets versus emerging markets. DCC-GARCH estimates the time-varying correlations of bivariate data, while GO-GARCH estimates the fine covariance structure using 13-variate data. Comparison between the methodologies produces a more detailed description of volatility interdependencies. Based on a horizon of 3,898 observations over a longer time, the analysis is enriched by detecting time–frequency interactions in wavelet coherence, facilitating an understanding of different time frames. The findings on Australian, Singaporean, Thai and Vietnamese spillovers provide trading participants and policymakers with additional evidence and information to manage risk in the context of market volatility across global markets.
Ben-AhmedK., TheiriS., & KasraouiN. (2023). Short-term effect of COVID-19 pandemic on cryptocurrency markets: A DCC-GARCH model analysis. Heliyon, 9(8).
2.
ChaudhryA. (2024). DCC-GARCH approach for detecting dynamic relation among selected green indices of indian stock market. Journal of Asian Energy Studies, 8, 38–50.
3.
CheemaM. A., & ScrimgeourF. (2019). Oil prices and stock market anomalies. Energy Economics, 83, 578–587. https://doi.org/10.1016/j.eneco.2019.08.003
4.
DasD., MaitraD., DuttaA., & BasuS. (2022). Financial stress and crude oil implied volatility: New evidence from continuous wavelet transformation framework. Energy Economics, 115, 106388. https://doi.org/10.1016/j.eneco.2022.106388
5.
GoyalP., & SoniP. (2025). Stock markets volatility during crises periods: a bibliometric analysis. Qualitative Research in Financial Markets, 17(1), 41–65. https://doi.org/10.1108/QRFM-06-2023-0143
6.
HongJ., YanY., KuruogluE. E., & ChanW. K. (2023). Multivariate time series forecasting with GARCH models on graphs. IEEE Transactions on Signal and Information Processing Over Networks, 9, 557–568.
7.
HuongL. T. M. (2024). The contagion between stock markets: Evidence from Vietnam and Asian emerging stocks in the context of COVID-19 Pandemic. Macroeconomics and Finance in Emerging Market Economies, 17(1), 78–94.
8.
JeribiA., & GhorbelA. (2022). Forecasting developed and BRICS stock markets with cryptocurrencies and gold: Generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis. International Journal of Emerging Markets, 17(9), 2290–2320.
9.
JooB. A., & GhulamY. A. (2023). The predictability, volatility persistence, and leverage effects in stock market returns: A study of BRICS stock market indices. American J of Finance and Accounting, 7(3–4), 188–213. https://doi.org/10.1504/ajfa.2023.136686
10.
KarimM. M., ChowdhuryM. A. F., & MasihM. (2022). Re-examining oil and BRICS’stock markets: New evidence from wavelet and MGARCH-DCC. Macroeconomics and Finance in Emerging Market Economies, 15(2), 196–214. https://doi.org/10.1080/17520843.2020.1861047
11.
KhanW., SharmaV., & AnsariS. A. (2022). Modeling the dynamics of oil and agricultural commodity price nexus in linear and nonlinear frameworks: A case of emerging economy. Review of Development Economics, 26(3), 1733–1784.
12.
KirikkaleliD., & SowahJ. K. (2020). A wavelet coherence analysis: Nexus between urbanization and environmental sustainability. Environmental Science and Pollution Research, 27, 30295–30305.
13.
KocaarslanB., & SoytasU. (2024). How do the reserve currency and uncertainties in major markets affect the uncertainty of oil prices over time?International Journal of Finance & Economics, 30(2), 2016–2041. https://doi.org/10.1002/ijfe.2962
14.
KörnerT. W. (2022). Fourier analysis. Cambridge university press.
15.
LeT. M. H., NguyenT. N. M., & TranT. Y. V. (2024). Spillover effects of oil price fluctuations on the US and Asia–Pacific stock markets: A multivariate EGARCH analysis. Asia-Pacific Financial Markets, 32(3), 1049–1076.
16.
LiuY., NaktnasukanjnN., TamprasirtA., & RattanadamrongaksornT. (2024). Do crude oil, gold and the US dollar contribute to Bitcoin investment decisions? An ANN-DCC-GARCH approach. Asian Journal of Economics and Banking, 8(1), 2–18. https://doi.org/10.1108/AJEB-10-2023-0106
17.
MensiW., NekhiliR., VoX. V., & KangS. H. (2021). Good and bad high-frequency volatility spillovers among developed and emerging stock markets. International Journal of Emerging Markets, 18(9), 2107–2132. https://doi.org/10.1108/IJOEM-01-2021-0074
18.
MishraA., & DashA. K. (2024). Return volatility of Asian stock exchanges; a GARCH DCC analysis with reference of Bitcoin and global crude oil price movement. Journal of Chinese Economic and Foreign Trade Studies, 17(1), 29–48.
19.
PengL., & LiangC. (2023). Sustainable development during the post-COVID-19 period: Role of crude oil. Resources Policy, 85, 103843.
20.
RamakrishnanS. (2024). Introductory chapter: Wavelet theory and modern applications. InModern applications of wavelet transform. IntechOpen.
21.
SharmaS. S., & NarayanP. K. (2022). Technology shocks and stock returns: A long-term perspective. Journal of Empirical Finance, 68, 67–83. https://doi.org/10.1016/j.jempfin.2022.06.002
22.
StojkovS., PucarE. B., GlavaškiO., & Beljic´M. (2023). Oil price spillover effects to the stock market sentiment: The case of higher vs. lower oil import EU countries. Economies, 11(11), 279.
23.
TarchellaS., KhalfaouiR., & HammoudehS. (2024). The safe haven, hedging, and diversification properties of oil, gold, and cryptocurrency for the G7 equity markets: Evidence from the pre- and post-COVID-19 periods. Research in International Business and Finance, 67, 102125. https://doi.org/10.1016/j.ribaf.2023.102125
24.
Van Der WeideR. (2002). GO-GARCH: A multivariate generalized orthogonal GARCH model. Journal of Applied Econometrics, 17(5), 549–564.
WuF., JiQ., MaY.-R., & ZhangD. (2024). Investor sentiments and extreme risk spillovers from oil to stock markets: Evidence from Asian countries. Journal of the Asia Pacific Economy, 29(3), 1257–1283.
27.
YousfiM., DhaouiA., & BouzgarrouH. (2021). Risk spillover during the COVID-19 global pandemic and portfolio management. Journal of Risk and Financial Management, 14(5), 222.
28.
ZhangY., & LiS. (2019). The impact of investor sentiment on crude oil market risks: Evidence from the wavelet approach. Quantitative Finance, 19(8), 1357–1371. https://doi.org/10.1080/14697688.2019.1581368
