The Information Architecture of Panic

1. Objective This paper establishes a behavioral-econometric framework to quantify the predictive capacity and asymmetrical transmission of a media-generated Global Fear Index (GFI) over conditional market volatility across strategic commodity assets (Energy, Metals, Agriculture). 2. Methodology & Execution Empirical Integration: Synchronized multi-asset futures market data (Yahoo Finance API) with high-frequency institutional panic proxies—CBOE Volatility Index (VIX) and Economic Policy Uncertainty Index (EPU)—via the FRED API (2018–2024). Directional Causality: Implemented stationary transformations and ANOVA-based Granger Causality testing across 1-to-3 day informational lag horizons. Asymmetric Volatility Regressions: Formulated an Exponential GARCH-inspired OLS interaction model decomposed into positive fear spikes ($\Delta \text{GFI}^+$) and negative cooling shifts ($\Delta \text{GFI}^-$) evaluated with Newey-West HAC robust standard errors. 3. Key Findings & Impact Information Lead Time: Proved that media-driven fear Granger-causes conditional volatility in energy markets (Crude Oil) with an explicit 1-to-3 day lead time ($p < 0.005$). The Asymmetry of Fear: Demonstrated that bad news destabilizes energy markets significantly faster than good news calms them ($\beta_{\text{Spike}} = 0.0072, p = 0.076$ vs $\beta_{\text{Drop}} = 0.0028, p = 0.428$). Asset Insulation: Verified that structural fear transmission is asset-bounded: while energy markets are hyper-sensitive to headline panic, Gold and Corn remain insulated due to their role as macro-anchors or seasonal physical supply chains.