Geopolitical Shocks and Market Resilience: Evidence from Commodity, Crude Oil, Natural Gas, and Gold Markets

Table 1 Variable description and data sourcesVariableRepresentationUnit of MeasurementData SourceCommodity IndexComd.Current spot price in USDS&P Goldman Sachs Commodity Index (S&P GSCI), Garman and Klass (1980)Crude Oil FuturesWTIUSD Per BarrelEnergy Information Administration https://www.eia.govNatural Gas FuturesGASUSD Per Million BtuEnergy Information Administration https://www.eia.govGoldGLDCurrent Price of Gold Per Ounce in USDhttps://goldprice.org/spot-gold.htmlGeopolitical Risk GlobalGPRFrequency of Newspaper Stories and Features Worldwidehttps://www.matteoiacoviello.com/gpr.htm Caldara and Iacoviello (2018)Geopolitical Risk RussiaGPR-RUSFrequency of Newspaper Stories and Features related to Russiahttps://www.matteoiacoviello.com/gpr.htm Caldara and Iacoviello (2018)Geopolitical Risk USAGPR-USAFrequency of Newspaper Stories and Features related to USAhttps://www.matteoiacoviello.com/gpr.htm Caldara and Iacoviello (2018)Daily Frequescy data from 1 January 2008, to 30 December 2024Data time SpanCrises MeasurementVolatility and SpilloverCOVID-19Event-1Crises MeasurementWHO announced COVID-19 (11 March 2020)Russia Ukraine ConflictEvent-2Crises MeasurementRussia Ukraine Conflict (24 February 2022)Data and MaterialsTo examine strategic commodity index deviations and analyze dynamic changes in crude oil price volatility, as well as variations in natural gas and gold prices, this study empirically examines their responses to multiple crises such as the COVID-19 pandemic and the Russia–Ukraine conflict that started in 2022, periods of heightened GPR; this study provides significant insights. The S&P commodity index (COMD) was selected as a benchmark due to its status as the most widely tracked global commodity index, with the ability to attract substantial investor capital, following the studies in [7,74]. The proposed index for commodities comprises five major commodity market sectors, ensuring broad representation. To represent conventional energy, this study employs WTI crude oil futures and natural gas futures, while for precious and industrial metals, the gold index is used. Table 1 provides a detailed description of the data sources and variables utilized.Using daily stock price return data over other frequency data provides higher granularity, better volatility measurement, and improved statistical power. It enables short-term trading strategies, the early detection of market shifts, and more accurate event detection such as geopolitical impacts. Daily frequency data further support estimations, which enhance liquidity analysis, portfolio management, and forecasting accuracy, making them ideal for capturing short-term market dynamics and responding to real-time changes that unfold with the preview of dynamics in the geopolitical landscape.The S&P commodity index incorporates production-weighted categories, making it a robust indicator of the beta coefficient for commodity markets [39]. This study utilizes total return data across all selected variables, including crude oil, natural gas, gold, and the composite commodity index. This study selected an important time period, spanning 1 January 2008 to 30 December 2024, carefully chosen to capture the volatility and spillover effects of multiple global crises, such as the 2008 global financial crisis, European banking Crisis (2009–2012), and Chinese Stock Market Crash (2015–2016), and the most distractive economic downturns of the COVID-19 Pandemic (2020), the ongoing Russia–Ukraine conflict (2022–present), and the emerging Middle East Crisis (2024) are examined in this study.To ensure the reliability of the estimation frequency, data volatility was calculated using closing prices from Monday to Friday, excluding weekends, providing a 5-day-per-week dataset. These calculations enabled precise volatility and spillover assessments, reflecting how GPRs transmitted by dominant global players, such as the USA and Russia, impact commodity markets, following the seminal work in [39]. The Time-Varying Parameter Vector Autoregressive (TVP-VAR) model proposed by [18–20] was applied to evaluate the commodity market’s responses to GPR. Figure 1 presents the weekly time series volatility for each commodity market. The findings reveal distinct volatility patterns, with notable peaks corresponding to specific global crises. For instance, the commodity and natural gas markets exhibited their highest volatility during the COVID-19 pandemic and the Russia–Ukraine conflict in 2022, respectively, which reflects the profound impacts of these crises on market dynamics. This comprehensive analysis underscores the importance of understanding commodity market behavior under heightened GPR, providing valuable insights for policymakers and investors in navigating such turbulent periods.