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

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.

8 pages, 15 figures, 1 table. Replaced by final version published in Physica A

References in corpus (2)

• Superpositions of Probability Distributions
• Microscopic Origin of Non-Gaussian Distributions of Financial Returns

Cited by in corpus (9)

• Universality Classes of Fluctuation Dynamics in Hierarchical Complex Systems
• Transitions between superstatistical regimes: validity, breakdown and applications
• Emergence of skewed non-Gaussian distributions of velocity increments in isotropic turbulence
• Spatio-temporal complexity of power-grid frequency fluctuations
• Asymmetric Tsallis distributions for modelling financial market dynamics
• Superstatistics with cut-off tails for financial time series
• Phase and amplitude synchronisation in power-grid frequency fluctuations in the Nordic Grid
• First passage time for superstatistical Fokker-Planck models
• Bayesian inference and superstatistics to describe long memory processes of financial time series