Effective multifractal features and l-variability diagrams of high-frequency price fluctuations time series

TL;DR

This paper investigates the multifractal characteristics of high-frequency stock price changes and volatility, revealing the roles of dependence and non-Gaussianity, and analyzing fractal properties of l-diagrams in financial time series.

Contribution

It provides a comprehensive analysis of multifractality in high-frequency financial data, highlighting the influence of dependence and non-Gaussianity, and examining the fractal dimension of l-diagrams.

Findings

Dependence and non-Gaussianity equally influence multifractality.

Fractal dimension of l-diagrams is independent of lag.

Multifractal analysis applies to high-frequency equity data.

Abstract

In this manuscript we present a comprehensive study on the multifractal properties of high-frequency price fluctuations and instantaneous volatility of the equities that compose Dow Jones Industrial Average. The analysis consists about quantification of dependence and non-Gaussianity on the multifractal character of financial quantities. Our results point out an equivalent influence of dependence and non-Gaussianity on the multifractality of time series. Moreover, we analyse l-diagrams of price fluctuations. In the latter case, we show that the fractal dimension of these maps is basically independent of the lag between price fluctuations that we assume.