Multifractal Cross-correlation Analysis Method Research Articles

Dynamics of asymmetric multifractal cross-correlations between cryptocurrencies and global stock markets: Role of gold and portfolio implications

Understanding the risk transmission mechanism between cryptocurrencies and global stock markets is crucial for investors' risk management strategies. This study delves into this relationship, drawing on the Fractal Market Hypothesis and acknowledging the nonlinearity and asymmetry present in financial market correlations. We introduce a combined approach, integrating Multifractal Detrended Partial Cross-Correlation Analysis (MF-DPCCA) and Asymmetric Multifractal Cross-Correlation Analysis (MF-ACCA), resulting in the Asymmetric Multifractal Partial Cross-Correlation Analysis (MF-APCCA) method. By applying this method, we aim to uncover the dynamics between cryptocurrencies and global stock markets. We focus on stock indices from E7 and G7 countries and consider the Bitcoin market for our analysis. Our initial findings, using the MF-ACCA method, reveal pronounced asymmetric cross-correlations between cryptocurrencies and these stock markets. Notably, the correlation strength between Bitcoin and the G7 stock markets surpasses that of Bitcoin and the E7 markets. Further, when we account for and remove the influence of the common factor, gold, our analysis with the MF-APCCA method indicates an enhanced long-memory cross-correlation between Bitcoin and these stock markets. This cross-correlation tends to amplify during periods of positive returns but shows anti-persistence during negative returns. Remarkably, these asymmetries become more pronounced during significant market shifts. When comparing Bitcoin's relationship with the G7 and E7 indices, the latter displays heightened asymmetric risk correlations in both upward and downward market phases. In conclusion, gold, recognized as a safe-haven asset, can serve as a buffer, diminishing the portfolio risk between Bitcoin and the stock market. These empirical findings bear significant weight, urging investors to reassess the intricate relationship between stock and cryptocurrency markets. It also underscores the importance of well-informed cross-market portfolio investments and the need for regulatory vigilance to prevent systemic financial pitfalls.

Insights from multifractality analysis of tanker freight market volatility with common external factor of crude oil price

This paper is intended for exploring the multifractal features of tanker freight rate market volatility with the common external factor of crude oil price by both the multifractal cross-correlation analysis method (MF-CCA) and the multifractal detrended partial cross-correlation analysis method (MF-DPXA) with consideration of finite size effect. The multifractal spectrums of original, random and surrogate time series are employed to separate the three components of multifractality, and to uncover the influence of financial crisis and oil price on volatility and cross-correlated fluctuations of the tanker freight rates. After the financial crisis in terms of the generalized Hurst exponent, stronger non-linear characteristic and noticeable anti-persistent characteristic in cross correlations between freight rates are supported by the MF-DPXA analysis. Meanwhile, stronger multifractality is indicated by the MF-CCA analysis. These results deepen the understanding of multifractality in tanker freight rate market, and provide investors, shipping related operators or even market players with insight to adjust their marketing strategies.

Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets.

A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series.

The application of the multifractal cross-correlation analysis methods in radar target detection within sea clutter

Abstract Many complex systems generate multifractal time series which are long-range cross-correlated. This paper introduces three multifractal cross-correlation analysis methods, such as multifractal cross-correlation analysis based on the partition function approach (MFXPF), multifractal detrended cross-correlation analysis (MFDCCA) methods based on detrended fluctuation analysis (MFXDFA) and detrended moving average analysis (MFXDMA), which only consider one moment order. We do comparative analysis of the artificial time series (binomial multiplicative cascades and Cantor sets with different probabilities) by these methods. Then we do a feasibility test of the fixed threshold target detection within sea clutter by applying the multifractal cross-correlation analysis methods to the IPIX radar sea clutter data. The results show that it is feasible to use the method of the fixed threshold based on the multifractal feature parameter Δ f ( α ) by the MFXPF and MFXDFA-1 methods. At last, we give the main conclusions and provide a valuable reference on how to choose the multifractal algorithms, the detection parameters and the target detection methods within sea clutter in practice.

Multifractal detrended cross-correlations between the Chinese exchange market and stock market

Based on the daily price data of the Chinese Yuan (RMB)/US dollar exchange rate and the Shanghai Stock Composite Index, we conducted an empirical analysis of the cross-correlations between the Chinese exchange market and stock market using the multifractal cross-correlation analysis method. The results demonstrate the overall significance of the cross-correlation based on the analysis of a statistic. Multifractality exists in cross-correlations, and the cross-correlated behavior of small fluctuations is more persistent than that of large fluctuations. Moreover, using the rolling windows method, we find that the cross-correlations between the Chinese exchange market and stock market vary with time and are especially sensitive to the reform of the RMB exchange rate regime. The previous reduction in the flexibility of the RMB exchange rate in July 2008 strengthened the persistence of cross-correlations and decreased the degree of multifractality, whereas the enhancement of the flexibility of the RMB exchange rate in June 2010 weakened the persistence of cross-correlations and increased the multifractality. Finally, several relevant discussions are provided to verify the robustness of our empirical analysis.