Research Article | | Peer-Reviewed

Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics

Received: 24 September 2024     Accepted: 15 October 2024     Published: 31 October 2024
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Abstract

Background: Price changes in economics present significant geometric challenges due to sharp discontinuities, which cannot be efficiently described by continuous processes like Brownian motion. Traditional models often rely on linear assumptions, yet financial data frequently exhibit irregular, complex patterns. Fractal theory, a mathematical framework, offers a more accurate way to describe these fluctuations by revealing the underlying self-similar structures in price changes and scaling phenomena. This study explores the use of fractal geometry to gain deeper insights into market behavior. Objective: The objective is to demonstrate that an alternative model, constructed based on geometric scaling assumptions, offers a more accurate description of price changes in competitive markets. Method: The study combined the scaling principle from fractal geometry with a stable Levy model to formulate an integrated model. The logarithmic transformation of the model was applied over successive price changes to observe the behavior of market prices. Result: The scaling principle asserts that no specific time interval (such as a day or a week) holds inherent significance in competitive markets. Instead, these time features are compensated or arbitrated away, supporting the idea that market behavior is self-similar across different time scales. Conclusion: The scaling principle provides a more reliable framework for modeling price changes and is recommended for consideration in economic analyses.

Published in American Journal of Theoretical and Applied Statistics (Volume 13, Issue 5)
DOI 10.11648/j.ajtas.20241305.16
Page(s) 175-180
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Fractal Theory, Scaling Principle, Levy Model, Price Changes

References
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[3] Mushunje L, Mashasha M, Chandiwana E. Estimating short-term returns with volatilities for high frequency stock trades in emerging economies using Gaussian processes (GPs). IntechOpen ebooks, 2021.
[4] Abuja Security and Commodity Exchange (ASCE) – Index on Commodity Pricing.
[5] Mandelbrot, B. B. The fractal geometry of nature. New York: Time Books; 1997.
[6] Akthtar N, Rajput Y, Tharewal S, Kale K. V, Mansa R. Fractal for Complexity Analysis of Diabetic Retinopathy in Retina Vasculature Images. International Journal of Research in Engineering and Technology. 2014;
[7] Chen, Y. Fractal modelling and fractal dimension description of urban morphology. Entropy. 2020; 22(9), 961,
[8] Taylor SJ. Modeling stochastic volatility: A review and comparative study. Math Finance 4(2): 183-204.
[9] Hussain, Murthy, Singh. Stock market volatility: A review of the empirical literature. IUJ J. Manag., 7, 96–105. 2019.
[10] Bhowmik, Wang. Stock market volatility and return analysis: A systematic literature review. Entropy 2020, 22, 522;
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Cite This Article
  • APA Style

    Abimbola, L. A., Adegoke, T. M., Oladoja, O. M. (2024). Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics. American Journal of Theoretical and Applied Statistics, 13(5), 175-180. https://doi.org/10.11648/j.ajtas.20241305.16

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    ACS Style

    Abimbola, L. A.; Adegoke, T. M.; Oladoja, O. M. Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics. Am. J. Theor. Appl. Stat. 2024, 13(5), 175-180. doi: 10.11648/j.ajtas.20241305.16

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    AMA Style

    Abimbola LA, Adegoke TM, Oladoja OM. Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics. Am J Theor Appl Stat. 2024;13(5):175-180. doi: 10.11648/j.ajtas.20241305.16

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  • @article{10.11648/j.ajtas.20241305.16,
      author = {Latifat Adebisi Abimbola and Taiwo Mobolaji Adegoke and Oladapo Muyiwa Oladoja},
      title = {Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {13},
      number = {5},
      pages = {175-180},
      doi = {10.11648/j.ajtas.20241305.16},
      url = {https://doi.org/10.11648/j.ajtas.20241305.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20241305.16},
      abstract = {Background: Price changes in economics present significant geometric challenges due to sharp discontinuities, which cannot be efficiently described by continuous processes like Brownian motion. Traditional models often rely on linear assumptions, yet financial data frequently exhibit irregular, complex patterns. Fractal theory, a mathematical framework, offers a more accurate way to describe these fluctuations by revealing the underlying self-similar structures in price changes and scaling phenomena. This study explores the use of fractal geometry to gain deeper insights into market behavior. Objective: The objective is to demonstrate that an alternative model, constructed based on geometric scaling assumptions, offers a more accurate description of price changes in competitive markets. Method: The study combined the scaling principle from fractal geometry with a stable Levy model to formulate an integrated model. The logarithmic transformation of the model was applied over successive price changes to observe the behavior of market prices. Result: The scaling principle asserts that no specific time interval (such as a day or a week) holds inherent significance in competitive markets. Instead, these time features are compensated or arbitrated away, supporting the idea that market behavior is self-similar across different time scales. Conclusion: The scaling principle provides a more reliable framework for modeling price changes and is recommended for consideration in economic analyses.},
     year = {2024}
    }
    

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  • TY  - JOUR
    T1  - Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics
    AU  - Latifat Adebisi Abimbola
    AU  - Taiwo Mobolaji Adegoke
    AU  - Oladapo Muyiwa Oladoja
    Y1  - 2024/10/31
    PY  - 2024
    N1  - https://doi.org/10.11648/j.ajtas.20241305.16
    DO  - 10.11648/j.ajtas.20241305.16
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 175
    EP  - 180
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20241305.16
    AB  - Background: Price changes in economics present significant geometric challenges due to sharp discontinuities, which cannot be efficiently described by continuous processes like Brownian motion. Traditional models often rely on linear assumptions, yet financial data frequently exhibit irregular, complex patterns. Fractal theory, a mathematical framework, offers a more accurate way to describe these fluctuations by revealing the underlying self-similar structures in price changes and scaling phenomena. This study explores the use of fractal geometry to gain deeper insights into market behavior. Objective: The objective is to demonstrate that an alternative model, constructed based on geometric scaling assumptions, offers a more accurate description of price changes in competitive markets. Method: The study combined the scaling principle from fractal geometry with a stable Levy model to formulate an integrated model. The logarithmic transformation of the model was applied over successive price changes to observe the behavior of market prices. Result: The scaling principle asserts that no specific time interval (such as a day or a week) holds inherent significance in competitive markets. Instead, these time features are compensated or arbitrated away, supporting the idea that market behavior is self-similar across different time scales. Conclusion: The scaling principle provides a more reliable framework for modeling price changes and is recommended for consideration in economic analyses.
    VL  - 13
    IS  - 5
    ER  - 

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