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

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  • Rani, Meenu
  • Garg, Bhavesh
  • Kumar, Arun

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.

Suggested Citation

  • Rani, Meenu & Garg, Bhavesh & Kumar, Arun, 2025. "Change point analysis in data with heavy tails: A Normal Inverse Gaussian approach," Economics Letters, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:ecolet:v:254:y:2025:i:c:s0165176525003143
    DOI: 10.1016/j.econlet.2025.112477
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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