It is ubiquitous in natural and social sciences that two variables, recorded temporally or spatially in a complex system, are cross-correlated and possess multifractal features. We propose a new method called multifractal detrended cross-correlation analysis (MF-DXA) to investigate the multifractal behaviors in the pow…
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48 results for “multifractal cross-correlation”
Many complex systems generate multifractal time series which are long-range cross-correlated. Numerous methods have been proposed to characterize the multifractal nature of these long-range cross correlations. However, several important issues about these methods are not well understood and most methods consider only o…
A new method for analyzing multifractal cross correlations in complex systems.
problem Characterizing long-range cross-correlations in complex systems.
method Multifractal Cross Wavelet Analysis (MFXWT)
result MFXWT accurately captures joint multifractality in binomial multifractal measures but may produce spurious results for bivariate fractional Brownian motions.
We introduce a new method for detection of long-range cross-correlations and multifractality - multifractal height cross-correlation analysis (MF-HXA) - based on scaling of qth order covariances. MF-HXA is a bivariate generalization of the height-height correlation analysis of Barabasi & Vicsek [Barabasi, A.L., Vicsek,…
New method detects intrinsic cross-correlations in non-stationary time series affected by common factors.
problem Bias in cross-correlation analysis due to common external factors.
method Multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA).
result MF-TWDPCCA accurately detects intrinsic cross-correlations between non-stationary time series.
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.
A new method detects long-range cross correlations in complex systems.
problem Detecting long-range cross correlations in complex systems.
method Joint multifractal analysis based on wavelet leaders (MF-X-WL).
result MF-X-WL detects cross correlations in synthetic and real-world data.
We propose a novel algorithm - Multifractal Cross-Correlation Analysis (MFCCA) - that constitutes a consistent extension of the Detrended Cross-Correlation Analysis (DCCA) and is able to properly identify and quantify subtle characteristics of multifractal cross-correlations between two time series. Our motivation for …
Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017q-fin.ST
Study on oil price's multifractal cross-correlations with other financial markets.
problem Analyzing statistical and multiscaling characteristics of oil prices and their cross-correlations with other financial instruments.
method Multifractal analysis, detrended cross-correlation coefficient, multifractal cross-correlation analysis.
result Multifractal cross-correlations between oil prices and other financial markets, especially with oil-producing countries' currencies.
Study on cryptocurrency trading patterns using multifractal analysis.
problem Lack of systematic study on temporal structure of cryptocurrency trading.
method Multifractal detrended cross-correlation analysis of price returns, trades, and volume.
result All analyzed quantities exhibit multifractal structure, both univariate and bivariate.
We report evidence of a deep interplay between cross-correlations hierarchical properties and multifractality of New York Stock Exchange daily stock returns. The degree of multifractality displayed by different stocks is found to be positively correlated to their depth in the hierarchy of cross-correlations. We propose…
An average instantaneous cross-correlation function is introduced to quantify the interaction of the financial market of a specific time. Based on the daily data of the American and Chinese stock markets, memory effect of the average instantaneous cross-correlations is investigated over different price return time inte…
New methods improve cross-correlation analysis of time series data.
problem Controversies in Multifractal detrended cross-correlation analysis.
method Proposes new options to handle negative cross-covariance.
result Improved robustness in multifractal spectrum analysis.
When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using classic detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover…
Study uses multifractal detrended cross-correlation to detect Forex arbitrage opportunities.
problem Detecting arbitrage opportunities in Forex markets.
method Multifractal detrended cross-correlation analysis applied to Forex time series.
result Strong cross-correlations found between exchange rates involved in triangular relations, including AUD and NZD.
Model allocates portfolios based on multifractal cross-correlations across different scales.
problem Heterogeneous scales and amplitude-dependent financial correlations.
method Constructs a portfolio allocation model using multifractal cross-correlation analysis (MFCCA) with signed fluctuation functions.
result Reduces tail risk and improves risk-adjusted performance compared to mean-variance model.
Study shows how COVID-19 pandemic affected China's crude oil futures market efficiency.
problem Impact of COVID-19 on China's crude oil futures market efficiency.
method Multifractal analysis to compare market efficiency before and during the pandemic.
result Market efficiency of SC and its cross-correlations with other assets increased significantly after the outbreak of COVID-19.
There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. The multifractal detrended cross-correlation analysis (MF-DCCA) approaches can be used to quantify such cross-correlations, such as the MF-DCCA based on detrend…
New analysis of stock market correlations reveals unique properties and optimal portfolio construction.
problem Understanding the unique properties of stock market correlations at different magnitudes.
method Used q-dependent cross-correlation analysis, random matrix theory, and complex network representation.
result Optimal multifractal order for portfolio optimization is approximately q=2.
Cryptocurrency market decouples from Forex, showing multifractality.
problem Decoupling of crypto market from Forex.
method High-frequency recordings and multiscale cross-correlations.
result Cryptocurrency market shows multifractality, decoupling from Forex.
Study uses detrended cross-correlation to analyze cryptocurrency market, revealing robust collective modes and distinguishing interdependencies.
problem Nonstationarity, long-range memory, and heavy-tailed fluctuations obscure traditional correlations in complex systems.
method Constructs detrended correlation matrices using multifractal detrended cross-correlation coefficient to emphasize different fluctuations.
result Detrending and fluctuation analysis reveal distinct spectral properties from random case, identifying market and sectoral components.
This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.
problem Exploring asymmetric multifractal cross-correlations in cryptocurrency markets.
method Fractal analysis and MF-ADCCA method to investigate asymmetric volatility dynamics.
result Cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH.
Paper proposes a new portfolio model for better investment decisions.
problem Traditional portfolio models fail to adapt to nonstationary markets.
method Developed a mean-detrended cross-correlation portfolio model (M-DCCP model).
result The M-DCCP model outperforms traditional models in constructing optimal portfolios.
In order to pursue the issue of the relation between the financial cross-correlations and the conventional Random Matrix Theory we analyse several characteristics of the stock market correlation matrices like the distribution of eigenvalues, the cross-correlations among signs of the returns, the volatility cross-correl…
Study shows multifractality emerging in decentralized cryptocurrency trading.
problem Understanding financial dynamics in decentralized cryptocurrency markets.
method Multifractal Detrended Fluctuation Analysis (MFDFA) on tick-by-tick transaction data.
result Multifractality is emerging in decentralized cryptocurrency trading, with larger fluctuations dominating.
Study cryptocurrency market complexity using multifractal and cross-correlation analyses.
problem Understanding the complexity and dynamics of cryptocurrency markets, especially during the COVID-19 pandemic.
method Multifractal formalism, cross-correlation analyses, network representation.
result Cryptocurrency market dynamics exhibit multifractal and intermittent bifractality, with topology changes during significant events.
Digital currencies exhibit multifractality due to heavy-tailed returns and temporal correlations.
problem Understanding market inefficiencies and predicting volatility in digital currencies.
method Multifractal cross-correlation analysis (MFCCA) and multifractal detrended fluctuation analysis (MFDFA).
result Temporal correlations are the primary source of multifractality in digital currency markets.
Minimum spanning tree filtering of correlations for varying time scales and size of fluctuationsq-fin.ST
A new method uses -dependent MSTs to analyze stock market correlations.
problem Analyzing correlations between different fluctuation amplitudes and time scales.
method Introduces -dependent minimum spanning trees (MST) based on -dependent detrended cross-correlation coefficients ().
result The MST graphs provide more information about correlation structure than conventional MSTs.
Stock market comovements are examined using cointegration, Granger causality tests and nonlinear approaches in context of mutual information and correlations. Underlying data sets are affected by non-stationarities and trends, we also apply AMF-DFA and AMF-DXA. We find only 170 pair of Stock markets cointegrated, and a…
Detecting unusual trading patterns on cryptocurrency exchanges by means of complexity measuresq-fin.TR
Study detects unusual trading patterns on crypto exchanges using complexity measures.
problem Detecting artificial trading activity on cryptocurrency exchanges.
method Complexity and statistical-structure measures derived from high-frequency trade-level data.
result Unusual trading patterns detected on Bitget for BTC and ETH after mid-May 2025.
We consider a few quantities that characterize trading on a stock market in a fixed time interval: logarithmic returns, volatility, trading activity (i.e., the number of transactions), and volume traded. We search for the power-law cross-correlations among these quantities aggregated over different time units from 1 mi…
Detrended fluctuation analysis made flexible to detect range of cross-correlated fluctuationsphysics.data-an
The detrended cross-correlation coefficient has recently been proposed to quantify the strength of cross-correlations on different temporal scales in bivariate, non-stationary time series. It is based on the detrended cross-correlation and detrended fluctuation analyses (DCCA and DFA, respectively) and c…
Study on price-volume correlation fractal features and market type effects.
problem Understanding the fractal features and market type effects of price-volume correlation.
method Applied MF-DXA method to analyze price, trading volume, and their coupling.
result Price, trading volume, and price-volume coupling exhibit power law and multifractal properties.
Cryptocurrencies show mature market characteristics but vary by size.
problem Understanding maturity in cryptocurrency markets.
method Quantitative analysis of return distributions, volatility, and correlations.
result Smaller cryptocurrencies lack mature market characteristics.
Multifractal analysis reveals complex patterns in financial markets.
problem Understanding the complex nonlinear nature of financial time series.
method Multifractal analysis methods and models applied to financial markets.
result Multifractality is ubiquitously observed in financial markets.
How much multifractality is included in monofractal signals?physics.data-an
We investigate the presence of residual multifractal background for monofractal signals which appears due to the finite length of the signals and (or) due to the long memory the signals reveal. This phenomenon is investigated numerically within the multifractal detrended fluctuation analysis (MF-DFA) for artificially g…
This paper examines multifractal dynamics in cryptocurrencies using two methodologies.
problem Understanding the multifractal nature of cryptocurrencies and their stochastic processes.
method Two alternative multi-scaling methodologies applied to 84 cryptocurrencies.
result Cryptocurrencies exhibit different degrees of long-range dependence and stochastic processes.
Many financial variables are found to exhibit multifractal nature, which is usually attributed to the influence of temporal correlations and fat-tailedness in the probability distribution (PDF). Based on the partition function approach of multifractal analysis, we show that there is a marked finite-size effect in the d…
A new method for direct determination of multifractal spectrum from moving average analysis.
problem Direct determination of multifractal spectrum from moving average analysis.
method Proposes a new approach based on detrending moving average method (MF-DMA) to directly determine multifractal spectrum.
result Direct determination of multifractal spectrum is possible with less computation cost compared to traditional approach.
We present a comparative analysis of multifractal properties of financial time series built on stock indices from developing (WIG) and developed (S&P500) financial markets. It is shown how the multifractal image of the market is altered with the change of the length of time series and with the economic situation on the…
Study examines multifractality in European power loads over 5 years.
problem Understanding multifractality in European power load time series.
method Applied Multifractal Detrended Fluctuation Analysis (MF-DFA) with improved methodology.
result European power loads exhibit multifractality in both distribution and autocorrelation functions.
Study finds intrinsic multifractality in maize and barley spot markets, but not in wheat and rice.
problem Understanding the complex price behavior of global grain spot markets.
method Utilized multifractal fluctuation analysis (MF-DFA) to investigate intrinsic multifractality.
result Intrinsic multifractality found in maize and barley sub-indices, but not in wheat and rice.
New study finds day-of-the-week effects in stock market returns using multifractal analysis.
problem Exploring calendar anomalies in stock markets, particularly day-of-the-week effects.
method Multifractal Detrended Fluctuation Analysis (MF-DFA) applied to daily returns of market indices.
result Monday returns exhibit more persistent behavior and richer multifractal structures than other days.
The multifractal detrended fluctuation analysis technique is employed to analyze the time series of gold consumer price index (CPI) and the market trend of three world's highest gold consuming countries, namely China, India and Turkey for the period: 1993-July 2013. Various multifractal variables, such as the generaliz…
The study finds significant power-law cross correlations in Bitcoin's return-volatility dynamics.
problem Investigating asymmetry in Bitcoin's return-volatility relationships.
method Analysis of daily and high-frequency Bitcoin data to identify cross correlations.
result Power-law cross correlations between returns and future volatilities are observed, indicating long-range dependencies.
Study reveals multifractal nature in Chinese stock markets and predicts future returns.
problem Predicting future stock market returns using multifractal characteristics.
method Multifractal detrended fluctuation analysis (MF-DFA) on high-frequency stock data.
result Spectral width Δα is a significant predictor of future stock returns.
Study analyzes foreign exchange rates using MFDFA, revealing multifractality and its sources.
problem Analyzing multifractality in foreign exchange rates.
method Multifractal Detrended Fluctuation Analysis (MFDFA) applied to shuffled and phase-randomized return series.
result Sources of multifractality differ among currencies: US dollar fat tails, British Pound and Euro long-range correlations, Japanese Yen broad tails.
Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analysesphysics.data-an
Wavelet analysis reveals limitations in detecting multifractality in signals with isolated singularities.
problem Detecting multifractality in signals with isolated singularities using detrended fluctuation analysis and wavelet leaders.
method Comparison of detrended fluctuation analysis and wavelet leaders on signals with isolated singularities.
result Signals with isolated singularities can artefactually give rise to broad multifractal spectra, leading to incorrect inference of multifractality.