Risk warning system for financial crises using multifractal analysis and dictionary learning

Walid E. AboElnasr, M.A. Zahran and Mohamed M. Abdelsalam

Chaos, Solitons & Fractals, 2025, vol. 201, issue P1

Abstract: The inherent complexity, non-linearity and dynamics of financial markets present significant impediments to effective early financial risk warning. While conventional models often fall short in capturing these intricacies, multifractal analysis provides a robust methodology for characterizing the complex scaling behaviors and heterogeneous dynamics inherent in financial time series. Crucially, observations indicate that specific multifractal features exhibit discernible patterns that differentiate pre-crisis periods from those during crises or extreme events. This research adopts dictionary learning as an unsupervised machine learning approach to codify these pre-crisis multifractal signatures. The objective is to develop a system that translates these learned patterns into timely and actionable alerts for impending extreme market conditions,thereby enhancing risk mitigation strategies.

Keywords: Financial crises; Early warning system; Multifractal analysis; Dictionary learning; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:201:y:2025:i:p1:s096007792501207x

DOI: 10.1016/j.chaos.2025.117194

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