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The origin of fat-tailed distributions in financial time series

Author

Listed:
  • Viswanathan, G.M.
  • Fulco, U.L.
  • Lyra, M.L.
  • Serva, M.

Abstract

A classic problem in physics is the origin of fat-tailed distributions generated by complex systems. We study the distributions of stock returns measured over different time lags τ. We find that destroying all correlations without changing the τ=1d distribution, by shuffling the order of the daily returns, causes the fat tails to almost vanish for τ>1d. We argue that the fat tails are caused by the well-known long-range volatility correlations that have already been systematically studied previously. Indeed, destroying only sign correlations, by shuffling the order of only the signs (but not the absolute values) of the daily returns, allows the fat tails to persist for τ>1d.

Suggested Citation

  • Viswanathan, G.M. & Fulco, U.L. & Lyra, M.L. & Serva, M., 2003. "The origin of fat-tailed distributions in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 329(1), pages 273-280.
    • Handle: RePEc:eee:phsmap:v:329:y:2003:i:1:p:273-280
    DOI: 10.1016/S0378-4371(03)00608-3
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    Citations

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    Cited by:

    1. Schinckus, Christophe, 2010. "Is econophysics a new discipline? The neopositivist argument," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3814-3821.
    2. de Souza, Jeferson & Duarte Queirós, Sílvio M., 2009. "Effective multifractal features of high-frequency price fluctuations time series and ℓ-variability diagrams," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2512-2521.
    3. Yang, Honglin & Wan, Hong & Zha, Yong, 2013. "Autocorrelation type, timescale and statistical property in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1681-1693.
    4. Christophe Schinckus, 2011. "What can econophysics contribute to financial economics?," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 58(2), pages 147-163, June.
    5. Cerqueti, Roy & Giacalone, Massimiliano & Panarello, Demetrio, 2019. "A Generalized Error Distribution Copula-based method for portfolios risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 687-695.
    6. Miguel A Fuentes & Austin Gerig & Javier Vicente, 2009. "Universal Behavior of Extreme Price Movements in Stock Markets," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-4, December.

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