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<p>Although the number of studies on the interactions between commodity prices and financial markets has been growing, the dynamic relationships between oil and gold prices and Southeast Asian stock markets remain insufficiently explored, particularly from a time-frequency perspective. This study examines how fluctuations in oil and gold prices affect equity markets in selected ASEAN countries, and vice versa, from January 2015 to December 2024. To this end, we combine the wavelet transform method with the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. The wavelet approach decomposes time series into multiple time scales, revealing frequency-variant connections between the variables. Subsequently, the DCC-GARCH model is applied to capture time-varying conditional correlations and market volatility spillovers. Our empirical results indicate that the impacts of oil and gold prices differ across countries and investment horizons, with stronger interactions during periods of global financial turbulence. These findings provide valuable insights for investors, policymakers, and risk managers seeking a deeper understanding of market dynamics in the context of increasing financial integration and external commodity shocks.</p>

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Dynamic Two-Way Interactions of Oil and Gold with Southeast Asian Stock Markets: A Wavelet-DCC GARCH Study

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Tram B. T. Tran,
  • Ho Hoang Gia Bao,
  • Hoang Phong Le,
  • Thanh An Vu,
  • Ba Hoang Nguyen,
  • Thanh Huu Phu Nguyen

摘要

Although the number of studies on the interactions between commodity prices and financial markets has been growing, the dynamic relationships between oil and gold prices and Southeast Asian stock markets remain insufficiently explored, particularly from a time-frequency perspective. This study examines how fluctuations in oil and gold prices affect equity markets in selected ASEAN countries, and vice versa, from January 2015 to December 2024. To this end, we combine the wavelet transform method with the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. The wavelet approach decomposes time series into multiple time scales, revealing frequency-variant connections between the variables. Subsequently, the DCC-GARCH model is applied to capture time-varying conditional correlations and market volatility spillovers. Our empirical results indicate that the impacts of oil and gold prices differ across countries and investment horizons, with stronger interactions during periods of global financial turbulence. These findings provide valuable insights for investors, policymakers, and risk managers seeking a deeper understanding of market dynamics in the context of increasing financial integration and external commodity shocks.