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Fat tails in financial return distributions revisited: Evidence from the Korean stock market

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  • Eom, Cheoljun
  • Kaizoji, Taisei
  • Scalas, Enrico

Abstract

This study empirically re-examines fat tails in stock return distributions by applying statistical methods to an extensive dataset taken from the Korean stock market. The tails of the return distributions are shown to be much fatter in recent periods than in past periods and much fatter for small-capitalization stocks than for large-capitalization stocks. After controlling for the 1997 Korean foreign currency crisis and using the GARCH filter models to control for volatility clustering in the returns, the fat tails in the distribution of residuals are found to persist. We show that market crashes and volatility clustering may not sufficiently account for the existence of fat tails in return distributions. These findings are robust regardless of period or type of stock group.

Suggested Citation

  • Eom, Cheoljun & Kaizoji, Taisei & Scalas, Enrico, 2019. "Fat tails in financial return distributions revisited: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119306442
    DOI: 10.1016/j.physa.2019.121055
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    Cited by:

    1. Leovardo Mata Mata & José Antonio Núñez Mora & Ramona Serrano Bautista, 2021. "Multivariate Distribution in the Stock Markets of Brazil, Russia, India, and China," SAGE Open, , vol. 11(2), pages 21582440211, April.
    2. Giulia Di Nunno & Kk{e}stutis Kubilius & Yuliya Mishura & Anton Yurchenko-Tytarenko, 2023. "From constant to rough: A survey of continuous volatility modeling," Papers 2309.01033, arXiv.org, revised Aug 2025.
    3. Eom, Cheoljun & Park, Jong Won, 2023. "Price behavior of small-cap stocks and momentum: A study using principal component momentum," Research in International Business and Finance, Elsevier, vol. 65(C).
    4. Matsushita, Raul & Nobre, Iuri & Da Silva, Sergio, 2026. "Beyond volatility: Using differential entropy to detect financial market regimes," Chaos, Solitons & Fractals, Elsevier, vol. 202(P2).
    5. Giulia Di Nunno & Kęstutis Kubilius & Yuliya Mishura & Anton Yurchenko-Tytarenko, 2023. "From Constant to Rough: A Survey of Continuous Volatility Modeling," Mathematics, MDPI, vol. 11(19), pages 1-35, October.
    6. EOM, Cheoljun & EOM, Yunsung & PARK, Jong Won, 2024. "Intermediate cross-sectional prospect theory value in stock markets: A novel method," International Review of Financial Analysis, Elsevier, vol. 93(C).
    7. Jinxin Cui & Elie Bouri, 2026. "Jumps and higher-order moments of crude oil and stock sectors in China: new insights from timescales connectedness," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-48, December.
    8. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2021. "Financial Return Distributions: Past, Present, and COVID-19," Papers 2107.06659, arXiv.org.
    9. Przemys{l}aw Rola, 2025. "Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets," Papers 2507.09734, arXiv.org.
    10. Changtai Li & Weihong Huang & Wei-Siang Wang & Wai-Mun Chia, 2023. "Price Change and Trading Volume: Behavioral Heterogeneity in Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 677-713, February.
    11. Eom, Cheoljun & Park, Jong Won, 2020. "Effects of the fat-tail distribution on the relationship between prospect theory value and expected return," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    12. Kewin Pk{a}czek & Damian Jelito & Marcin Pitera & Agnieszka Wy{l}oma'nska, 2025. "Statistical applications of the 20/60/20 rule in risk management and portfolio optimization," Papers 2504.02840, arXiv.org.
    13. Fernandes, Leonardo H.S. & Figueirôa, José R.A. & Martins, Caleb M.F. & Martins, Adriel M.F., 2025. "The battle of informational efficiency: Cryptocurrencies vs. classical assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 664(C).
    14. Massing, Till & Ramos, Arturo, 2021. "Student’s t mixture models for stock indices. A comparative study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    15. Giuseppe Brandi & Tiziana Di Matteo, 2026. "Multiscaling in the Rough Bergomi Model: A Tale of Tails," Papers 2601.11305, arXiv.org.
    16. Eom, Cheoljun & Eom, Yunsung & Park, Jong Won, 2023. "Left-tail momentum and tail properties of return distributions: A case of Korea," International Review of Financial Analysis, Elsevier, vol. 87(C).
    17. Eom, Cheoljun & Kaizoji, Taisei & Livan, Giacomo & Scalas, Enrico, 2021. "Limitations of portfolio diversification through fat tails of the return Distributions: Some empirical evidence," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    18. Echaust, Krzysztof & Just, Małgorzata, 2022. "Is gold still a safe haven for stock markets? New insights through the tail thickness of portfolio return distributions," Research in International Business and Finance, Elsevier, vol. 63(C).
    19. Burns, Christopher B. & Kane, Stephen, 2022. "Arbitrage breakdown in WTI crude oil futures: An analysis of the events on April 20, 2020," Resources Policy, Elsevier, vol. 76(C).

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