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arXiv · 2610.23476

Closely spaced change-point detection with an application to geopolitical shocks in oil and gas markets

Nachiketas Waychal·Arnab Kumar Laha·Ankur Sinha
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Abstract

Change points that arrive in quick succession, or near the boundaries of the sample, are prone to type-II error: the few observations trapped between successive breaks carry too little information for global procedures to analyze. This article develops a test statistic, $I_1$, for which we derive the limiting null distribution under Gaussian noise, extend the theory to weakly dependent sequences, and prove consistency under a mean-shift alternative. Around $I_1$, we construct DCI, a two-phase algorithm that couples a global scan with multiple local tests via an adaptive-width sliding window. In extensive simulations, $I_1$ and DCI remain Pareto-optimal over criteria spaces spanning true and false positive rates, localization error, and segmentation covering. An application to the US-Iran 2026 conflict quantifies the exposure of energy markets to geopolitical shocks using two new metrics, event sensitivity and event attribution, leading to time series tokenization.

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Nachiketas Waychal, Arnab Kumar Laha, Ankur Sinha. 2026-10-16. Closely spaced change-point detection with an application to geopolitical shocks in oil and gas markets. https://arxiv.org/abs/2610.23476

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