Cross-Market Volatility in Response to Trump’s Trade Policy

• Syafira Ulya Firza

  Universitas Sumatera Utara, Indonesia and Universitas Mikroskil, Indonesia

  https://orcid.org/0009-0000-3941-927X (unauthenticated)

• Agustina

  Universitas Sumatera Utara, Indonesia and Universitas Mikroskil, Indonesia

  https://orcid.org/0009-0004-8987-6646 (unauthenticated)

• Iskandar Muda

  Universitas Sumatera Utara, Indonesia

  https://orcid.org/0000-0002-1713-6313 (unauthenticated)

• Sambas Ade Kesuma

  Universitas Sumatera Utara, Indonesia

  https://orcid.org/0000-0003-0236-6500 (unauthenticated)

• Noor Marini Haji Abdullah

  Universiti Teknologi MARA, Cawangan Kelantan, Malaysia

  https://orcid.org/0000-0002-9502-6013 (unauthenticated)

This study investigates the volatility responses across multiple financial markets to Donald Trump's trade policy announcements, focusing on key indices (DJIA, SSEC, IHSG), exchange rates (USD/CNY, USD/IDR), commodities (Gold), and cryptocurrency (Bitcoin). Employing models from the GARCH family, namely GARCH (1,1), GJR GARCH (1,1), and EGARCH (1,1), the research analyzes daily time series data to capture volatility clustering, leverage effects, and asymmetry in market reactions. The findings indicate that model suitability varies across financial assets. EGARCH (1,1) provides the best fit for DJIA, IHSG, USD/CNY, and Bitcoin, effectively capturing asymmetric volatility patterns. In contrast, GJR GARCH (1,1) is more suitable for SSEC, USD/IDR, and Gold. Bitcoin and DJIA exhibit higher volatility in response to trade policy shocks, while Gold and USD/IDR remain relatively stable, suggesting safe haven characteristics. These results highlight the importance of applying advanced volatility models to better understand market sensitivities during periods of uncertainty related to international trade interventions. The findings offer useful insights for investors and policymakers in managing cross-market risk.

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