When Historical Models Fail Under Geopolitical Uncertainty, Dynamic Implied Probability Outperforms the statistical parametric models

Key Findings

This paper demonstrate that in environments characterized by geopolitical stress, energy market disruption, and supply chain uncertainty, the real-time options surface systematically dominates any backward-looking statistical framework as a source of forward-looking distributional intelligence.

Abstract

This paper examines the informational superiority of Dynamic Implied Probability (DIP), a realtime risk-neutral distribution extraction framework developed by Shimko and Babaei (2025) over conventional statistical parametric models during periods of acute geopolitical uncertainty, using Advanced Micro Devices (AMD).