Extreme values and fat tails of multifractal fluctuations

TL;DR

This paper investigates the extreme value behavior of multifractal processes, highlighting the limitations of classical methods due to correlations, and proposes a unified framework for financial data tail analysis.

Contribution

It introduces a novel approach to analyze extreme events in multifractal data, accounting for correlations and non self-averaging properties, with applications to finance.

Findings

Classical tail estimators are unreliable for correlated multifractal data.

Multifractal processes exhibit non self-averaging extreme statistics.

The framework explains multiscaling, volatility clustering, and the inverse cubic law in financial returns.

Abstract

In this paper we discuss the problem of the estimation of extreme event occurrence probability for data drawn from some multifractal process. We also study the heavy (power-law) tail behavior of probability density function associated with such data. We show that because of strong correlations, standard extreme value approach is not valid and classical tail exponent estimators should be interpreted cautiously. Extreme statistics associated with multifractal random processes turn out to be characterized by non self-averaging properties. Our considerations rely upon some analogy between random multiplicative cascades and the physics of disordered systems and also on recent mathematical results about the so-called multifractal formalism. Applied to financial time series, our findings allow us to propose an unified framemork that accounts for the observed multiscaling properties of return fluctuations, the volatility clustering phenomenon and the observed ``inverse cubic law'' of the return pdf tails.