Transitions between superstatistical regimes: validity, breakdown and applications

TL;DR

This paper introduces a multi-scale generalization of superstatistics to better model complex systems, demonstrates a transition between regimes with financial data, and discusses its limitations and connections to other processes.

Contribution

It proposes a versatile multi-scale superstatistics framework and provides empirical evidence of regime transitions in financial data, extending the applicability of superstatistics.

Findings

Identified a transition between two superstatistics regimes in financial data.

Showed limitations of canonical superstatistics in fractional diffusion processes.

Discussed connections with Brownian subordination.

Abstract

Superstatistics is a widely employed tool of non-equilibrium statistical physics which plays an important role in analysis of hierarchical complex dynamical systems. Yet, its "canonical" formulation in terms of a single nuisance parameter is often too restrictive when applied to complex empirical data. Here we show that a multi-scale generalization of the superstatistics paradigm is more versatile, allowing to address such pertinent issues as transmutation of statistics or inter-scale stochastic behavior. To put some flesh on the bare bones, we provide a numerical evidence for a transition between two superstatistics regimes, by analyzing high-frequency (minute-tick) data for share-price returns of seven selected companies. Salient issues, such as breakdown of superstatistics in fractional diffusion processes or connection with Brownian subordination are also briefly discussed.