scholarly journals Seeking a Chaotic Order in the Cryptocurrency Market

2019 ◽  
Vol 24 (2) ◽  
pp. 36 ◽  
Author(s):  
Samet Gunay ◽  
Kerem Kaşkaloğlu
Keyword(s):  
Initial Conditions ◽  
Largest Lyapunov Exponent ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
The Public ◽  
Box Counting ◽  
The Media ◽  
Bds Test ◽  
Detrended Fluctuation

In this study, we investigate the existence of chaos in the global cryptocurrency market. Specifically, we analyze parameters of chaotic order, nonlinearity, sensitivity to the initial conditions, monofractality, and multifractality. For this purpose, we conduct a comprehensive series of tests, including Brock–Dechert–Scheinkman (BDS) test, largest Lyapunov exponent, box-counting, and monogram analysis for fractal dimension, and multiple tests for long-range dependence (Aggregated Variances, Peng, Higuchi, R/S Analysis, and Multifractal Detrended Fluctuation Analysis (MFDFA)). All tests are performed over a variety of major cryptocurrencies: Bitcoin, Litecoin, Ethereum, and Ripple. The empirical results support the existence of chaos in the cryptocurrency market. Accordingly, cryptocurrency returns are not random and follow a chaotic order. Therefore, long term predictions are not possible, contrary to most of the discussions ongoing in the media and the public.

scholarly journals Price Appreciation and Roughness Duality in Bitcoin: A Multifractal Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2088
Author(s):  
Cristiana Vaz ◽  
Rui Pascoal ◽  
Helder Sebastião
Keyword(s):  
Fluctuation Analysis ◽  
Informational Efficiency ◽  
Comprehensive Overview ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Long Range Correlations ◽  
The Public ◽  
Long Run ◽  
Short Run ◽  
The Relationship

Since its launch in 2009, bitcoin has thrived, attracting the attention of investors, regulators, academia, and the public in general. Its price dynamics, characterized by extreme volatility, severe jumps, and impressive long-term appreciation, suggest that bitcoin is a new digital asset. This study presents a comprehensive overview of the fractality of bitcoin in a high-frequency framework, namely by applying Multifractal Detrended Fluctuation Analysis (MF-DFA) and a Multifractal Regime Detecting Method (MRDM) to Bitstamp 1 min bitcoin returns from January 2013 to July 2020. The results suggest that bitcoin is multifractal, with smaller and larger fluctuations being persistent and anti-persistent, respectively. Multifractality comes from significant long-range correlations, which cast some doubts on the informational efficiency at this frequency, but mainly comes from fat-tails, which highlights the significant risks undertaken by investors in this market. Our most important result is that the degree and richness of multifractality is time-varying and increased after 2017, when volumes and prices experienced an explosive behaviour. This complexity puts into perspective the duality of bitcoin: while it is characterized by long-run attractiveness and increasing valuation, it also has a high short-run instability. Hence, this study provides some empirical evidence supporting the relationship between these two observable features.

A LOOK AT SHORT- AND LONG-TERM NONLINEAR DYNAMICS IN FAMILY BUSINESS STOCK RETURNS LISTED ON CASABLANCA STOCK EXCHANGE

Fractals ◽  
2019 ◽  
Vol 27 (08) ◽  
pp. 1950140
Author(s):  
SALIM LAHMIRI
Keyword(s):  
Stock Returns ◽  
Family Business ◽  
Stock Exchange ◽  
Largest Lyapunov Exponent ◽  
Fluctuation Analysis ◽  
Short Term ◽  
Short And Long Term ◽  
Moroccan Family ◽  
Detrended Fluctuation

Moroccan family business companies are becoming an attractive investment in Casablanca stock exchange. In this study, presence of fractal and chaos in Moroccan family business stock returns is examined. In particular, return series are analyzed by stationary wavelet transform to decompose original series into long- and short-term variations. Then, detrended fluctuation analysis and artificial neural networks are used to estimate, respectively Hurst exponent and largest Lyapunov exponent. Results from fractal analysis show that short variations in returns are anti-persistent, whilst long variations are persistent. Besides, results from chaos tests reveal that both short and long variations in returns are chaotic. Such conclusions should be taken into account in active portfolio optimization and management.

Understanding long-term persistence and multifractal behaviors in river runoff: A detailed study over China

2020 ◽  
Author(s):  
Naiming Yuan ◽  
Wenlu Wu ◽  
Fenghua Xie ◽  
Yanjun Qi
Keyword(s):  
River Runoff ◽  
Detrended Fluctuation Analysis ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Precipitation Data ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Runoff Change ◽  
Catchment Areas ◽  
Detrended Fluctuation

<p><span>Long-term persistence (LTP) and multifractality in river runoff fluctuations have been well recognized over the recent decades, but the origins of these characteristics are still under debate. In this study, runoff and precipitation data from China are analyzed using detrended fluctuation analysis (DFA) and its generalized version, multifractal detrended fluctuation analysis (MF-DFA). By comparing the results between runoff and the nearby precipitation data, we find the multifractal behaviors in river runoff may be propagated from the nearby precipitation data, but the LTP is not inherited from precipitation. The LTP in river runoff may arise from the spatial aggregation effect, as it is closely related with the catchment area, especially for stations with large catchment areas. These findings are based on data from China, which was not analyzed systematically due to the poor data availability. Since the existence of LTP and multifractality makes the runoff change not completely random, one should further introduce these characteristics into hydrological models, for improved water managements and better estimations of hazard risks.</span></p>

scholarly journals UNDERSTANDING ASIAN EMERGING STOCK MARKETS

2019 ◽  
pp. 495-510 ◽  
Author(s):  
Shaista Arshad ◽  
Syed Aun Raza Rizvi ◽  
Omair Haroon
Keyword(s):  
Negative Impact ◽  
Market Development ◽  
Fluctuation Analysis ◽  
Time Varying ◽  
Short Term ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Long Term Stability ◽  
Emerging Stock Markets ◽  
Detrended Fluctuation

We use a three-step process employing multifractal detrended fluctuation analysis tostudy time-varying changes in the volatility and efficiency of Asian emerging equitymarkets. Our findings suggest that, in emerging markets, long-term stability andefficiency are linked to market development and liberalization. Our findings furthersuggest that financial crises have a negative impact on the efficiency of emergingmarkets but only in the short term.

Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019)

2021 ◽  
pp. 126118
Author(s):  
Javier Gómez-Gómez ◽  
Rafael Carmona-Cabezas ◽  
Ana B. Ariza-Villaverde ◽  
Eduardo Gutiérrez de Ravé ◽  
Francisco José Jiménez-Hornero
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

ARRHYTHMIA DIAGNOSIS FROM ECG SIGNAL ANALYSIS USING STATISTICAL FEATURES AND NOVEL CLASSIFICATION METHOD

2021 ◽  
pp. 2150025
Author(s):  
SAURAV MANDAL ◽  
NABANITA SINHA
Keyword(s):  
Cardiac Arrhythmia ◽  
Present Model ◽  
Fluctuation Analysis ◽  
Statistical Features ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Ecg Signals ◽  
Proposed Model ◽  
Electrocardiogram Ecg ◽  
Detrended Fluctuation ◽  
Heart Condition

This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.

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