Version 1
: Received: 24 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (12:14:28 CEST)
How to cite:
Gottimukkala, S. R. Applying the Multifractal Model of Asset Returns (MMAR) to Financial Markets: Insights and Limitations. Preprints2024, 2024091986. https://doi.org/10.20944/preprints202409.1986.v1
Gottimukkala, S. R. Applying the Multifractal Model of Asset Returns (MMAR) to Financial Markets: Insights and Limitations. Preprints 2024, 2024091986. https://doi.org/10.20944/preprints202409.1986.v1
Gottimukkala, S. R. Applying the Multifractal Model of Asset Returns (MMAR) to Financial Markets: Insights and Limitations. Preprints2024, 2024091986. https://doi.org/10.20944/preprints202409.1986.v1
APA Style
Gottimukkala, S. R. (2024). Applying the Multifractal Model of Asset Returns (MMAR) to Financial Markets: Insights and Limitations. Preprints. https://doi.org/10.20944/preprints202409.1986.v1
Chicago/Turabian Style
Gottimukkala, S. R. 2024 "Applying the Multifractal Model of Asset Returns (MMAR) to Financial Markets: Insights and Limitations" Preprints. https://doi.org/10.20944/preprints202409.1986.v1
Abstract
This study presents an application of fractal mathematics to financial markets through the utilization of the Multifractal Model of Asset Returns (MMAR). The model is employed to analyse a 30-year dataset encompassing three distinct financial markets: the USD/NOK currency pair, the OMXS30 stock index, and the 12-month LIBOR rate. The MMAR model successfully captures essential stylized facts about financial markets, including high kurtosis, non-independent price movements, and clustered volatile days. Simulations generated by the model demonstrate realistic behaviour and multifractal characteristics. However, the model lacks a statistical method for assessing its accuracy and may underestimate kurtosis in high-kurtosis markets. Notably, the model is capable of producing simulations across a wide range of kurtosis values and can be instrumental in stress-testing portfolios. Despite its potential, the model is subject to limitations, such as the need for human judgment, ambiguous predictions, and interpretability issues. Furthermore, the model may become unreliable following a high-kurtosis event.
Keywords
Financial; Kurtosis; Multifractal Model of Asset Returns; Portfolios
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
