Herding Behavior in Commodity Markets During Geopolitical Conflict: Evidence from the Iran Conflict Escalations (2024–2026)

Military conflict generates a qualitatively distinct category of market shock that is sudden, geographically concentrated, and channeled directly through physical energy supply routes. This paper examines investor herding and cross-sectional return dispersion across five commodity markets (Brent crude, WTI crude, Henry Hub natural gas, spot gold, and the Baltic Dry Index) using 475 daily observations from January 2024 through April 2026, covering the sustained escalation phase of the Iran–Israel conflict. The empirical analysis incorporates eight complementary specifications: (1) baseline CSAD regression; (2) GARCH(1,1) conditional volatility augmentation; (3) volatility regime partitioning (high versus low); (4) quantile regression across the CSAD distribution; (5) asset-level disaggregation; (6) interaction with the geopolitical risk (GPR) index; (7) asymmetric analysis distinguishing between up- and down-market conditions; and (8) rolling 240-day estimation to capture time-varying dynamics. The results tend to reject the herding hypothesis and provide suggestive evidence of positive cross-commodity dispersion. The baseline model shows that large market movements significantly increase cross-sectional dispersion. At the asset level, natural gas exhibits the highest dispersion coefficient, reflecting its structural independence from oil-related geopolitical fundamentals. Moreover, gold provides evidence consistent with a positive but comparatively smaller coefficient, consistent with its role as a stabilizing safe-haven asset. Dispersion effects are broadly symmetric across market conditions. Furthermore, the geopolitical risk index does not exert a significant marginal effect. However, the analysis is restricted to five commodity assets and a single geopolitical conflict episode (the Iran–Israel conflict), which may limit the generalizability of the findings to other markets or conflict contexts.