Beyond volatility: Using differential entropy to detect financial market regimes

Raul Matsushita, Iuri Nobre and Sergio Da Silva

Chaos, Solitons & Fractals, 2026, vol. 202, issue P2

Abstract: Financial markets alternate between calm and turbulent phases, but volatility alone often fails to reveal structural changes in uncertainty. This study develops a differential entropy approach that detects market regimes through shifts in distributional complexity rather than moment-based variability. The method employs a data-adaptive heavy-tailed kernel, yielding a consistent and scale-invariant entropy estimator that remains valid under dependent dynamics. Monte Carlo experiments confirm its robustness and sensitivity to tail transitions. When applied to major stock indices from 1998 to 2025, the entropy-based indicator identifies heavy-tailed regimes that align with well-known episodes of financial distress, including the Dot-com Bubble, Global Financial Crisis, COVID-19 shock, and the 2025 tariff crisis, while Gaussian regimes characterize periods of market efficiency. The results demonstrate that variance and entropy may diverge during crises, indicating that entropy captures forms of uncertainty invisible to volatility. Overall, the framework offers a nonparametric and information-theoretic tool for regime detection and systemic risk assessment in complex financial systems.

Keywords: Differential entropy; Heavy-tailed kernel; Extreme events; Tail regimes (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p2:s0960077925015668

DOI: 10.1016/j.chaos.2025.117553

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