Change point analysis in data with heavy tails: A Normal Inverse Gaussian approach

Meenu Rani, Bhavesh Garg and Arun Kumar

Economics Letters, 2025, vol. 254, issue C

Abstract: This article examines change points using the Normal Inverse Gaussian distribution that effectively captures heavy tails and skewness. We analyze the daily returns of fourteen major global stock indices from 2018 to 2024. Our methodology combines the Modified Information Criterion with Seeded Binary Segmentation and Greedy Selection. The analysis detects 7–17 change points per index, with primary clusters (5–12 indices) and secondary clusters (3–4 indices). Of note, the largest cluster emerges during the COVID-19 pandemic, underscoring methodology’s effectiveness in identifying change points and the interconnectedness of global markets during crises. The findings also indicate increased market independence after the pandemic.

Keywords: COVID-19; Change points; Greedy selection; Modified information criterion; Normal inverse Gaussian distribution; Seeded binary segmentation (search for similar items in EconPapers)
JEL-codes: C12 C15 C46 C58 C63 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:254:y:2025:i:c:s0165176525003143

DOI: 10.1016/j.econlet.2025.112477

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