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Transition from lognormal to chi-square superstatistics for financial time series

Author

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  • Dan Xu
  • Christian Beck
Abstract
Share price returns on different time scales can be well modelled by a superstatistical dynamics. Here we provide an investigation which type of superstatistics is most suitable to properly describe share price dynamics on various time scales. It is shown that while chi-square superstatistics works well on a time scale of days, on a much smaller time scale of minutes the price changes are better described by lognormal superstatistics. The system dynamics thus exhibits a transition from lognormal to chi-square superstatistics as a function of time scale. We discuss a more general model interpolating between both statistics which fits the observed data very well. We also present results on correlation functions of the extracted superstatistical volatility parameter, which exhibits exponential decay for returns on large time scales, whereas for returns on small time scales there are long-range correlations and power-law decay.

Suggested Citation

  • Dan Xu & Christian Beck, 2015. "Transition from lognormal to chi-square superstatistics for financial time series," Papers 1506.01660, arXiv.org, revised Mar 2016.
    • Handle: RePEc:arx:papers:1506.01660
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    Cited by:

    1. Karina Arias-Calluari & Morteza. N. Najafi & Michael S. Harr'e & Fernando Alonso-Marroquin, 2019. "Stationarity of the detrended price return in stock markets," Papers 1910.01034, arXiv.org, revised Aug 2020.
    2. Yusuke Uchiyama & Takanori Kadoya, 2018. "Superstatistics with cut-off tails for financial time series," Papers 1809.04775, arXiv.org.
    3. Kosun, Caglar & Ozdemir, Serhan, 2016. "A superstatistical model of vehicular traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 466-475.
    4. Markelov, Oleg & Nguyen Duc, Viet & Bogachev, Mikhail, 2017. "Statistical modeling of the Internet traffic dynamics: To which extent do we need long-term correlations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 48-60.
    5. Geoffrey Ducournau, 2021. "Bayesian inference and superstatistics to describe long memory processes of financial time series," Papers 2105.04171, arXiv.org.
    6. Devi, Sandhya, 2021. "Asymmetric Tsallis distributions for modeling financial market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    7. Sandhya Devi, 2021. "Asymmetric Tsallis distributions for modelling financial market dynamics," Papers 2102.04532, arXiv.org.
    8. Arias-Calluari, Karina & Najafi, Morteza. N. & Harré, Michael S. & Tang, Yaoyue & Alonso-Marroquin, Fernando, 2022. "Testing stationarity of the detrended price return in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).

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