Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets

Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday dat...

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Main Authors: Haider Ali, Muhammad Aftab, Faheem Aslam, Paulo Ferreira
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Fractal and Fractional
Subjects:
Online Access:https://www.mdpi.com/2504-3110/8/10/571
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author Haider Ali
Muhammad Aftab
Faheem Aslam
Paulo Ferreira
author_facet Haider Ali
Muhammad Aftab
Faheem Aslam
Paulo Ferreira
author_sort Haider Ali
collection DOAJ
description Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.
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spelling doaj-art-1024df15c1d34794bd609638d3ad063d2025-08-20T02:11:04ZengMDPI AGFractal and Fractional2504-31102024-09-0181057110.3390/fractalfract8100571Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex MarketsHaider Ali0Muhammad Aftab1Faheem Aslam2Paulo Ferreira3Department of Management Sciences, COMSATS University, Park Road, Islamabad 45550, PakistanDepartment of Management Sciences, COMSATS University, Park Road, Islamabad 45550, PakistanSchool of Business Administration, Al Akhawayan University, Ifrane 53000, MoroccoVALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, PortugalJump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.https://www.mdpi.com/2504-3110/8/10/571jumpsmultifractalitycomplexityMFDFArolling windowcryptocurrencies
spellingShingle Haider Ali
Muhammad Aftab
Faheem Aslam
Paulo Ferreira
Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
Fractal and Fractional
jumps
multifractality
complexity
MFDFA
rolling window
cryptocurrencies
title Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
title_full Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
title_fullStr Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
title_full_unstemmed Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
title_short Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
title_sort inner multifractal dynamics in the jumps of cryptocurrency and forex markets
topic jumps
multifractality
complexity
MFDFA
rolling window
cryptocurrencies
url https://www.mdpi.com/2504-3110/8/10/571
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AT pauloferreira innermultifractaldynamicsinthejumpsofcryptocurrencyandforexmarkets