HomeJournal of Interdisciplinary Perspectivesvol. 4 no. 7 (2026)

Market Volatility Interactions and Safe-Haven Behavior in ASEAN-5 Equity Markets: Evidence from Global Commodity Shocks (2015–2025)

Jet Siegbert Oregano

Discipline: Finance

 

Abstract:

Utilizing an asymmetric time-varying volatility modeling framework (DCC-GJR-GARCH) estimated under a Multivariate Student-t distribution, this study investigates market volatility interactions and safehaven behavior across the ASEAN-5 equity markets in response to global gold and crude oil shocks from January 2015 to December 2025. The study captures non-linear crisis dynamics across 2,608 synchronized trading days, spanning the 2020 COVID-19 pandemic crash and the 2022–2025 global inflationary cycle. Daily adjusted closing prices were extracted from Yahoo Finance via the tidyquant API in R. Preliminary diagnostics confirm the stationarity of all return series and the presence of significant volatility clustering, supporting the asymmetric modeling approach. Three principal findings emerge. First, significant positive leverage effects are documented across all ASEAN-5 equity markets (γ = .05 to .12, p < .001), confirming structural overreaction to negative market shocks. Second, gold exhibits a statistically significant negative gamma parameter (γ = −.05, p < .001), indicating counter-cyclical, safe-haven behavior; however, its hedging efficacy is markedly heterogeneous—functioning as a strong crisis shield for Singapore (r = −.15) and Indonesia (r = −.13) but acting as a mere diversifier for the Philippines (r = −.04). Third, Brent crude oil exhibits significant positive contagion toward oil-importing nations, particularly Thailand (r = .15) and Singapore (r = .12), while Malaysia demonstrates comparatively lower sensitivity (r = .08). Portfolio optimization reveals that institutional investors require substantially elevated gold allocations (44.72%–70.31%) to achieve minimum-variance portfolios. These findings suggest that standard Western allocation norms are insufficient for the ASEAN-5 risk environment, with important implications for regional fund managers and policymakers seeking to design robust hedging strategies during periods of global market stress.



References:

  1. Akhtaruzzaman, M., Boubaker, S., Lucey, B., & Sensoy, A. (2021). Is gold a hedge or a safe-haven asset in the COVID-19 crisis? Economic Modelling, 102, 105588. https://doi.org/10.1016/j.econmod.2021.105588
  2. Ali, M., Alam, N., & Rizvi, S.A. (2020). Coronavirus (COVID-19)—An epidemic or pandemic for financial markets. Journal of Behavioral and Experimental Finance, 27, 100341. https://doi.org/10.1016/j.jbef.2020.100341
  3. Baba, B. (2024). Spillovers of good and bad volatility in Asian emerging markets: Insights from global and regional perspectives. Journal of Economics and Finance, 48(4), 925–949. https://doi.org/10.1007/s12197-024-09696-5
  4. Baker, S., Bloom, N., & Davis, S. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
  5. Basher, S.A., & Sadorsky, P. (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH, Energy Economics, 54,©, 235–247. https://doi.org/10.1016/j.eneco.2015.11.022
  6. Baur, D., & Smales, L. (2020). Hedging geopolitical risk with precious metals. Journal of Banking & Finance, 117, 105823. https://doi.org/10.1016/j.jbankfin.2020.105823
  7. Bonini, S., Huang, S., & Simaan, M. (2026). Watching the FedWatch. Journal of Futures Markets, 46: 675–697. https://doi.org/10.1002/fut.70077
  8. Boritz, J.E., & No, W.G. (2020). How significant are the differences in financial data provided by key data sources? A comparison of XBRL, Compustat, Yahoo! Finance, and Google Finance.Journal of Information Systems, 34(3), 47–75. https://doi.org/10.2308/isys-52618
  9. Dancho, M., & Vaughan, D. (2023). tidyquant: Tidy quantitative financial analysis [Computer software]. Vienna, Austria: R Foundation for Statistical Computing. https://CRAN.R-project.org/package=tidyquant
  10. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
  11. Ghalanos, A. (2022). rmgarch: Multivariate GARCH models [Computer software]. Vienna, Austria: R Foundation for Statistical Computing. https://CRAN.R- project.org/package=rmgarch
  12. Glosten, L., Jagannathan, R., & Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779– 1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
  13. Kroner, K., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28(4), 535–551. https://doi.org/10.2307/2331164
  14. Kumar, A., & Padakandla, S.R. (2022). Testing the safe-haven properties of gold and bitcoin in the backdrop of COVID-19: A wavelet quantile correlation approach. Finance Research Letters, 47(PB). https://doi.org/10.1016/j.frl.2022.102707
  15. Mensi, W., Nekhili, R., Vo, X.V., & Kang, S.H. (2021). Oil and precious metals: Volatility transmission, hedging, and safe haven analysis from the Asian crisis to the COVID-19 crisis. Economic Analysis and Policy, 71©, 73–96. https://doi.org/10.1016/j.eap.2021.04.009
  16. Mensi, W., Yousaf, I., Vo, X.V., & Kang, S. (2023). Spillovers and connectedness between Chinese and ASEAN stock markets during bearish and bullish market statuses. International Journal of Emerging Markets, 19(10), 2661–2690. https://doi.org/10.1108/IJOEM-07-2022-1194
  17. Pham, T.N., Luong, K.L., Thuy, L.N., & Do, T.T.N. (2023). Safe haven for Asian equity markets during financial distress: Bitcoin versus gold. Acta Informatica Pragensia, 12(2), 400–418. Prague University of Economics and Business. https://doi.org/10.18267/j.aip.224
  18. R Core Team. (2023). R: A language and environment for statistical computing [Computer software]. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/
  19. Robiyanto, R., Nugroho, B.A., Handriani, E., & Huruta, A.D. (2020). Hedge effectiveness of put replication, gold, and oil on ASEAN-5 equities. Financial Innovation, 6(1), 1–29. https://doi.org/10.1186/s40854-020-00199-w
  20. Salim, K., Disli, M., Nagayev, R., Ilyas, A., & Aysan, A. (2024). A pandemic’s grip: Volatility spillovers in Asia Pacific equity markets during the onset of Covid-19. Borsa Istanbul Review, 24(5), 898–907. https://doi.org/10.1016/j.bir.2024.05.001
  21. Salisu, A., Raheem, I., & Ndako, U. (2020). The inflation hedging properties of gold, stocks and real estate: A comparative analysis. Resources Policy, 66, 101605. https://doi.org/10.1016/j.resourpol.2020.101605
  22. Sarwar, S., Tiwari, A.K., & Cao, T. (2020). Analyzing volatility spillovers between oil market and Asian stock markets. Resources Policy, 66, 101608. https://doi.org/10.1016/j.resourpol.2020.101608
  23. Singh, R.K., Singh, Y., Kumar, S., Kumar, A., & Alruwaili, W. (2024). Mapping risk–return linkages and volatility spillover in BRICS stock markets through the lens of linear and non-linear GARCH models. Journal of Risk and Financial Management, 17(10), 437. https://doi.org/10.3390/jrfm17100437
  24. Vo, X.V., & Tran, T.T.A. (2020). Modelling volatility spillovers from the US equity market to ASEAN stock markets. Pacific-Basin Finance Journal, 59, 101246. https://doi.org/10.1016/j.pacfin.2019.101246
  25. Wen, X., & Cheng, H. (2018). Which is the safe haven for emerging stock markets, gold or the US dollar? Emerging Markets Review, 35, 69–90. https://doi.org/10.1016/j.ememar.2017.12.006
  26. Yahoo Finance Help Center. (2024). How Yahoo Finance calculates adjusted closing prices. https://help.yahoo.com/kb/SLN28256.html
  27. Zeng, J., & Wu, J. (2025). Cross-market volatility spillovers between China and the United States: A DCC EGARCH-t-Copula framework with out-of-sample forecasting. PLOS One, 20(10), e0333794. https://doi.org/10.1371/journal.pone.0333794