How a Geopolitical Shock to the Strait of Hormuz Can Be Modeled with High‑Dimensional Machine Learning
Research preview
The sudden claim that the Strait of Hormuz “belongs to the United States” creates an immediate risk premium for oil‑related assets and a cascade of spill‑over effects across global markets. For quantitative traders, the event is a natural laboratory for applying high‑dimensional regression, regularisation, and debiased machine‑learning techniques to capture both the short‑run price shock and the longer‑run equilibrium adjustments. 1. Translating a Political Announcement into Data Signals The first step is to turn the news feed into a structured set of predictors. Typical sources include real‑time news sentiment scores, geopolitical risk indices, oil‑price futures, forward curves, and macro‑variables such as inventory levels and USD strength....
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