Fractal Analysis of S&P 500 Sector Indexes

Baki ÃœNAL

Fiscaoeconomia, 2023, issue 3

Abstract: In this study multifractal properties of S&P 500 sector indexes are investigated with Multifractal Detrended Fluctuation Analysis (MF-DFA). The MF-DFA is a signal processing technique that is used to describe the multifractal properties of a time series data. It is an extension of Detrended Fluctuation Analysis (DFA), which is a widely utilized method for estimating the scaling behavior of a time series. Main idea behind MF-DFA is to decompose a time series into multiple scales using a coarse-graining procedure, and then to estimate the scaling behavior of each scale using DFA. This gives a set of scaling exponents that describe the multifractal features of the time series. Our MF-DFA results indicates the presence of multifractality in all S&P 500 sector indexes. Since these indexes are multifractal, we can conclude that they possess properties such as scaling variability, nonlinear dynamics, self-similarity, long-range dependence, multiscale correlations and nonstationary.

Keywords: Multifractality; MF-DFA; Multifractal Detrended Fluctuation Analysis; Fractal Theory; S&P 500 (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:fis:journl:230315

DOI: 10.25295/fsecon.1303067

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