Аннотации:
Financial markets are characterized by complex and often unpredictable dynamics, presenting significant challenges for investors, analysts, and policymakers. In recent years, fractal theory has emerged as a powerful tool for understanding the intricate patterns and behaviors exhibited by financial time series data. This paper provides a comprehensive review of the application of fractal theory in financial time series analysis, examining its theoretical foundations, empirical applications, and practical implications. Through a synthesis of relevant literature, we explore the utility of fractal techniques such as fractal dimension estimation, detrended fluctuation analysis (DFA), and multifractal analysis in quantifying the long-range dependence, self-similarity, and scaling properties of financial time series. Additionally, we discuss the implications of fractal dynamics for risk management, portfolio optimization, and market microstructure analysis, highlighting opportunities for future research and innovation in this evolving field.
