Abstract
This paper introduces an innovative method for segmented multifractal analysis, aimed at investigating the (in-) efficiency of major global economic sectors. The proposed approach, a modified version of the Multifractal Detrended Fluctuation Analysis (MF-DFA) technique, integrates change-point detection, followed by a segmentation of time series data, just before conducting multifractal measurements at various intervals. This novel method is applied to five indices representing the global financial and economic landscape, including the Standard and Poor 500 index, the Euro/USD exchange rate, Bitcoin’s price, crude oil prices, and the price of gold. The empirical findings reveal substantial structured multifractality, with particular prominence observed in the two commodity price indicators. These results prompt inquiries into the influence of significant events on the efficiency of economic and financial markets. The segmented multifractal analysis opens up new avenues for exploring the dynamics and resilience of these sectors, thereby enhancing our comprehension of their intricate behaviors and responses to diverse stimuli.
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