Fractal Structures and Multiscale Dynamics in Financial Markets

A Special Issue of Fractal and Fractional (ISSN 2504-3110) belonging to the section "Complexity".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 1398

Editors


E-Mail Website
Guest Editor
School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Interests: fractal; data-driven financial innovations

E-Mail Website
Guest Editor
Department of Business Administration, Pusan National University, Busan 46241, Republic of Korea
Interests: AI; ML; econophysics in finance; fractal

E-Mail Website
Guest Editor
Department of Industrial Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: business analytics; econophysics; financial engineering; portfolio management; time series; fractal
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Financial markets are complex systems characterized by nonlinear interactions, long-range dependence, and multiscale fluctuations. Fractal and multifractal analysis provide a fundamental framework for uncovering the intrinsic scaling properties and structural organization of financial time series, offering insights into market efficiency, volatility persistence, price formation mechanisms, and systemic stability.

This Special Issue focuses on the theoretical foundations, methodological developments, and empirical applications of fractal-based approaches in financial markets, with particular emphasis on understanding the structural and dynamical properties of market data. Contributions exploring the economic interpretation of fractal measures, such as the Hurst exponent, multifractal spectra, and scaling laws, are especially encouraged.

While data-driven and computational techniques may be employed as complementary tools, the primary aim of this issue is to advance the fractal perspective on financial market dynamics, bridging quantitative analysis with economic and financial insights.

This Special Issue welcomes original research and review articles that explore the intersection of fractal analysis, financial modeling, and AI-driven techniques. Topics of interest include, but are not limited to, the following:

  • Multifractal analysis;
  • Hurst exponent;
  • Fractal-based volatility modeling;
  • Fractal network analysis and market interdependencies;
  • Machine learning and AI approaches incorporating fractal features;
  • Data-driven models integrating fractal methodologies.

Dr. Poongjin Cho
Dr. Minhyuk Lee
Dr. Jae Wook Song
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fractal and Fractional is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multifractal analysis
  • hurst exponent
  • fractal-based volatility modeling
  • fractal network analysis and market interdependencies
  • machine learning and AI approaches incorporating fractal features
  • data-driven models integrating fractal methodologies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

30 pages, 5678 KB  
Article
A Multifractal Cross-Correlation Framework for Cryptocurrency Pairs Trading with CAPM Filtering
by Anil Thapa and Poongjin Cho
Fractal Fract. 2026, 10(8), 530; https://doi.org/10.3390/fractalfract10080530 - 3 Aug 2026
Viewed by 507
Abstract
The cryptocurrency market is characterized by high volatility and strong systematic co-movements among assets, posing significant challenges for conventional statistical arbitrage strategies. Effective pair trading in this environment requires the identification of return variation that is unexplained by common market factors. This study [...] Read more.
The cryptocurrency market is characterized by high volatility and strong systematic co-movements among assets, posing significant challenges for conventional statistical arbitrage strategies. Effective pair trading in this environment requires the identification of return variation that is unexplained by common market factors. This study proposes a hybrid framework that combines CAPM-based market-model filtering with multifractal detrended cross-correlation analysis (MFDCCA) for cryptocurrency pair trading. A single-index market-model regression is first employed to remove the linear market component, and the resulting market-model-filtered series are then analyzed using MFDCCA to characterize multifractal cross-correlation structures, which are subsequently used for pair selection and signal generation. The results show that the proposed MFDCCA-based approach effectively captures nonlinear and scale-dependent relationships in the market-model-filtered series, leading to improved risk-adjusted trading performance under the evaluated experimental settings. Compared with conventional dependence measures such as Pearson correlation, cointegration, and standard DCCA, the proposed framework achieves favorable risk-adjusted returns under the evaluated experimental settings. These findings highlight the effectiveness of combining market-model filtering with multifractal analysis for statistical arbitrage in cryptocurrency markets and demonstrate its potential to support portfolio management and risk control. Full article
(This article belongs to the Special Issue Fractal Structures and Multiscale Dynamics in Financial Markets)
Show Figures

Figure 1

24 pages, 743 KB  
Article
Chaos–Fractal–Entropy Dynamics and Regime Switching in Energy and Financial Markets: MS-VECM and MS-VARDL Methods
by Melike E. Bildirici and Elçin Aykaç Alp
Fractal Fract. 2026, 10(7), 448; https://doi.org/10.3390/fractalfract10070448 - 30 Jun 2026
Viewed by 432
Abstract
Understanding complex systems requires analytical tools capable of covering nonlinear dynamics, structural complexity, and informational uncertainty simultaneously. In this context, chaos theory, fractal analysis, and entropy measures provide complementary perspectives for examining any irregular behavior in natural and socio-economic systems. This paper examined [...] Read more.
Understanding complex systems requires analytical tools capable of covering nonlinear dynamics, structural complexity, and informational uncertainty simultaneously. In this context, chaos theory, fractal analysis, and entropy measures provide complementary perspectives for examining any irregular behavior in natural and socio-economic systems. This paper examined the relation between the Geopolitical Risk Index and the World Uncertainty Index to the volatility of West Texas Intermediate crude oil, gold, and Bitcoin over the period October 2010–February 2026. The analysis was motivated by the recent intensification of geopolitical tensions, particularly conflicts involving Iran, the United States, and Israel, which have significantly heightened uncertainty in global energy and financial markets. The empirical analysis first investigated the underlying complexity of the variables using entropy, chaos, and fractionality measures. Results from the Shannon, R-T entropy, Kolmogorov–Sinai complexity, Hurst, H-M and Lo’s R/S statistics, Phillips, and GPH fractionality tests consistently indicate entropy, fractal persistence, and long-range dependence across the series. In addition, the largest Lyapunov exponents and Hurst coefficients confirmed the presence of chaotic dynamics. The results reveal strong regime heterogeneity with geopolitical shocks exerting significantly stronger effects during high-uncertainty periods. Forecast comparisons show that regime-switching models outperform linear specifications, highlighting the importance of fractal and nonlinear dynamics in understanding financial market responses to geopolitical risk. Full article
(This article belongs to the Special Issue Fractal Structures and Multiscale Dynamics in Financial Markets)
Show Figures

Figure 1

Back to TopTop