Cross-correlation Behavior Research Articles

Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

Real-time and robust feature detection of continuous marker pattern for dense 3-D deformation measurement

Visuotactile sensing technology has received much attention in recent years. This article proposes a feature detection method applicable to visuotactile sensors based on continuous marker patterns (CMP) to measure 3-d deformation. First, we construct the feature model of checkerboard-like corners under contact deformation, and design a novel double-layer circular sampler. Then, we propose the judging criteria and response function of corner features by analysing sampling signals’ amplitude-frequency characteristics and circular cross-correlation behavior. The proposed feature detection algorithm fully considers the boundary characteristics retained by the corners with geometric distortion, thus enabling reliable detection at a low calculation cost. The experimental results show that the proposed method has significant advantages in terms of real-time and robustness. Finally, we have achieved the high-density 3-d contact deformation visualization based on this detection method. This technique is able to clearly record the process of contact deformation, thus enabling inverse sensing of dynamic contact processes.

Insights into asynchronous changes of cell wall polymers accumulated in different cell types during conifer xylem differentiation

An improved understanding of the events involved in cell wall polymers deposition during xylem development could provide new scientific ways for molecular regulation and biomass utilization. Axial and radial cells are spatially heterogeneous and have highly cross-correlated developmental behavior, whereas the deposition of corresponding cell wall polymers during xylem differentiation is less studied. To clarify our hypothesis that cell wall polymers of two cell types accumulated asynchronously, we performed hierarchical visualization, including label-free in situ spectral imaging of different polymer compositions during the development of Pinus bungeana. In axial tracheids, the deposition of cellulose and glucomannan was observed on earlier stages of secondary wall thickening than that of xylan and lignin, while xylan distribution was strongly related to spatial distribution of lignin during differentiation. The content of lignin and polysaccharides increased by over 130 % and 60 % respectively when the S3 layer was formed, compared to the S2 stage. In ray cells, the deposition of crystalline cellulose, xylan, and lignin was generally lagged compared to that in corresponding axial tracheids, although the process followed a similar order. The concentration of lignin and polysaccharides in ray cells was only approximately 50 % of that in the axial tracheids during secondary wall thickening.

Multifractal cross-correlation analysis between crude oil and agricultural futures markets: evidence from Russia–Ukraine conflict

PurposeThis study investigates the impacts of the Russia–Ukraine conflict on the cross-correlation between agricultural commodity prices and crude oil prices.Design/methodology/approachThe authors used MultiFractal Detrended Fluctuation Cross-Correlation Analysis (MF-X-DFA) to explore the correlation behavior before and during conflict. The authors analyzed the price connections between future prices for crude oil and agricultural commodities. Data consists of daily futures price returns for agricultural commodities (Corn, Soybean and Wheat) and Crude Oil (Brent) traded on the Chicago Mercantile Exchange from Aug 3, 2020, to July 29, 2022.FindingsThe results suggest that cross-correlation behavior changed after the conflict. The multifractal behavior was observed in the cross correlations. The Russia–Ukraine conflict caused an increase in the series' fractal strength. The study findings showed that the correlations involving the wheat market were higher and anti-persistent behavior was observed.Research limitations/implicationsThe study was limited by the number of observations after the Russia–Ukraine conflict.Originality/valueThis study contributes to the literature that investigates the impact of the Russia–Ukraine conflict on the financial market. As this is a recent event, as far as we know, we did not find another study that investigated cross-correlation in agricultural commodities using multifractal analysis.

Unimodular Perfect and Nearly Perfect Sequences: a Variation of Björck’s Scheme

Constant Amplitude (CA), Zero Auto Correlation (ZAC) sequences (or CAZAC sequences, aka perfect sequences) have numerous applications. We generalize the CAZAC notion to what we term as CASAC by permitting small autocorrelations (SAC). We extend Björck’s classification result of two-valued CAZAC sequences by providing a complete classification of all almost 2-valued (i.e., two-valued except for the first position which uses a third value) CASAC sequences. While Björck’s original work dealt only with primes p, we extend his ideas to any abelian group of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v\equiv 1\pmod {4}$ </tex-math></inline-formula> , as opposed to restricting just to the prime fields GF(p). Björck sequences have better ambiguity function than Zadoff-Chu sequences, making them suitable for radar and communications applications in the presence of high Doppler shifts. In fact, the discrete narrow band ambiguity function has an optimal bound in case of Björck sequences (as opposed to Gauss sequences). A one-parameter infinite family of CASAC we construct would have applications in Multiple-Input Multiple-Output (MIMO) areas. Toward MIMO applications, we introduce a performance measure we term as cross merit factor to study cross correlation behavior, generalizing the well-known notion of Golay Merit Factor (GMF).

ASYMMETRIC MULTIFRACTAL CROSS-CORRELATION DYNAMICS BETWEEN FIAT CURRENCIES AND CRYPTOCURRENCIES

This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior [Formula: see text]. Moreover, COVID-19 promoted an increase in the multifractal spectrum’s width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.

Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis

This study combines Seasonal-Trend decomposition using LOESS (STL) and multifractal cross-correlation analysis (MFCCA) to estimate the cross-correlations between the evolution of the economic policy uncertainty (EPU) of the USA and return of precious and industrial metals. Based on daily closing values of EPU and eight metals ranging from 01-Jan-2010 to 02-May-2021, the empirical findings confirm the presence of nonlinear dependencies, based on all EPU-Metal relationships, suggesting that EPU and metal markets are interlinked. Furthermore, there is evidence of multifractality, although with a varying degree, between EPU and metal markets. The strongest multifractality is found in EPU-Tin and EPU-Silver, while EPU-Platinum and EPU-Gold exhibit the weakest multifractality. Precious metals show persistent cross-correlations while industrial metals have anti-persistent cross-correlations with EPU, with that cross-correlation behavior being more persistent in the case of small fluctuations, which have different implications for the different market agents.

Multifractal Fluctuation Analysis of Correlations between Agricultural Futures Markets in China and the US Based on MF-X-DFA and MF-DPXA Methods

The fluctuation characteristics of the correlations between China and the US agricultural futures markets have attracted extensive attention from academic circles and government departments. As the main factor that affects the agricultural futures price, the impact of international crude oil futures price on the correlations of the Sino-US agricultural futures markets is also worth discussing. Therefore, this paper adopts the multifractal detrended cross-correlation analysis (MF-X-DFA) and multifractal detrended partial cross-correlation analysis (MF-DPXA) to explore the fluctuation characteristics of cross-correlations for China and the US agricultural futures markets before and after removing the West Texas Intermediate (WTI) crude oil futures price as well as the impact on the cross-correlations. The results show that the fluctuation characteristics of the cross-correlations and partial cross-correlations between the corresponding varieties of China and the US agricultural futures markets as well as among the varieties within the markets are multifractal. The cross-correlation behaviors and the cross-market risks are all affected to varying degrees by the West Texas Intermediate (WTI) crude oil futures. The West Texas Intermediate (WTI) crude oil futures weaken the cross-market risk of the Sino-US soybean futures, while strengthening the cross-market risk of the Sino-US corn and wheat futures. In addition, the impact of the West Texas Intermediate (WTI) crude oil futures on the cross-market risks among China agricultural futures is greater than those among the US corresponding agricultural futures.

Is the cross-correlation of EU carbon market price with policy uncertainty really being? A multiscale multifractal perspective

This paper aims to examine the cross-correlation relationship between EU carbon market price and the economic policy uncertainty. The United Kingdom and the United State of America are chosen as the representative countries. We first conduct the linear analysis to explore the correlation of EU carbon market futures return with the economic policy uncertainty of the two countries. Our findings show that there is no linear correlation between EU carbon market return and economic policy uncertainty. Then, we apply the multifractal detrended cross-correlation analysis to examine the cross-correlation between the return of EU carbon market futures and economic policy uncertainty. The empirical results indicate that the cross-correlations really exist, and the cross-correlation behavior structure over different carbon trading phases are not the same. Moreover, the empirical results show that the anti-persistence between the EU carbon futures return and economic policy uncertainty changes from the UK and the USA are both relatively strong. The findings provide deeper insights and management implications for the carbon market from a new perspective.

Multifractal Cross-correlations between foreign exchange rates and interest rate spreads

We apply the Multifractal detrended moving average analysis (MF-DMA) and the Multifractal Cross-Correlation Analysis(MFCCA) to study the cross-correlation behaviors between foreign exchange markets and interest rate differentials. Our empirical results obtained from the cross-correlation test, qDCCA coefficient and MFCCA confirm the existence of strong multifractality cross-correlations between foreign exchange rates and interest rate differentials. Foreign exchange rate has a stronger cross-correlation with short-term interest rate differentials in Australia, Canada, Japan, the United Kingdom (UK), and European Union (EU), and with long-term interest rate differentials in China, respectively. The results of rolling window analysis suggest varying cross-correlations between the exchange rates and interest rate differentials during the sample period. In addition, the cross-correlations between exchange rates and interest spreads vary with period, country and maturity of the spreads.

Higher-Order Multifractal Detrended Partial Cross-Correlation Analysis for the Correlation Estimator

In this paper, we develop a new method to measure the nonlinear interactions between nonstationary time series based on the detrended cross-correlation coefficient analysis. We describe how a nonlinear interaction may be obtained by eliminating the influence of other variables on two simultaneous time series. By applying two artificially generated signals, we show that the new method is working reliably for determining the cross-correlation behavior of two signals. We also illustrate the application of this method in finance and aeroengine systems. These analyses suggest that the proposed measure, derived from the detrended cross-correlation coefficient analysis, may be used to remove the influence of other variables on the cross-correlation between two simultaneous time series.

Cross-correlation complexity and synchronization of the financial time series on Potts dynamics

The Potts dynamic system is investigated to reproduce and analyze the fluctuation and noises of the financial markets in the present paper. A random financial agent-based model is developed by applying the stochastic dynamic systems to uncover the cross-correlation complexity and synchronization between different markets. In attempt to verify the rationally of the established model, the descriptive statistical analysis and cross-correlation of return behaviors between different series are exhibited by comparing with the actual market returns, SSE, SZSE, HSI, N225, GDAX and IXIC. Furthermore, the multiscale cross-sample entropy (MSCE) analysis and the multiscale generalized complexity synchronization (MGCS) analysis are employed to measure the cross-correlation between different series at multiple scales, where the MGCS method is firstly introduced to verify the synchrony behaviors between simulative series and actual series. Empirical results reveal that the feasibility and effectiveness of the proposed financial model, and exhibit the cross-correlation and synchronization behaviors among the return series from the perspective of complexity analysis.

Multifractal detrended partial cross-correlation analysis on Asian markets

The impact of the recession on global markets motivates us to explore the effect of the stock market on the economic stability of a country. The growth of a stock market depends on various factors such as inter-dependency, globalization, economic policies etc. In this paper, we apply the recently developed multifractal detrended partial cross-correlation analysis method on some Asian markets by assuming NASDAQ composite index commonly influences these markets. For this purpose, we analyze the daily returns of three Asian markets such as China’s Shanghai composite index (SHCOMP), Japan’s Nikkei stock average index (NIKKEI 225), and India’s Bombay stock exchange (BSE) sensex index over a period of 18 years (2000–2018). The analysis was performed between all bivariate time series after removing the external influence factor and the results indicate that there exists long-range partial cross-correlation behavior and multifractal nature. For comparison, we also performed analysis without removal of external influence factor i.e. multifractal detrended cross-correlation analysis. From the calculated Hurst scaling exponents, we found that the NASDAQ composite index shows significant influence on these analyzed Asian stock markets.

Multifractal detrended cross-correlation analysis and frequency dynamics of connectedness for energy futures markets

In this paper, the correlations among four major energy futures markets, i.e. electricity, coal, natural gas and crude oil, are investigated by multifractal detrended cross-correlation analysis (MF-DCCA) and a novel frequency connectedness method. The empirical results show that, firstly, there are significant multifractality in the cross-correlations among the four energy futures markets, and the cross-correlation behavior of small fluctuations is more persistent than that of large fluctuations. Secondly, the connectedness among the four energy markets is much stronger in short term than those in longer time horizons. Thirdly, in terms of the static connectedness measurement without time frequency and on time horizon of 4 to 10 days, electricity futures market dominates other energy markets by contributing the largest positive net connectedness in the system. Finally, when the dynamic connectedness is considered, we find that at different time periods, the four energy markets play various roles in the system by offering time-varying positive or negative net connectedness to other markets. The electricity futures market, however, transmits the largest positive net connectedness in recent years, especially on time horizons longer than 4 days.

Unified Probabilistic Modeling of Wind Reserves for Demand Response and Frequency Regulation in Islanded Microgrids

Islanded microgrids provide unique challenges due to the lack of transmission support, requiring local energy supply and grid regulation architecture. This paper presents a unified probabilistic assessment of wind reserves for demand response (DR) and frequency regulation in islanded microgrids. A multivariate nonparametric kernel density estimation algorithm is used to generate the probabilistic models of the wind resource, electrical demand, and predicted performance of wind generation. These models are numerically combined to evaluate the capability of wind generation to act as a dynamic reserve and predict its performance for DR, secondary generation, and frequency regulation in an islanded system. The probabilistic model captures multivariate cross-correlation, nonstationary environmental and load behavior, as well as multimodality in their underlying probability distributions. A case study is conducted to validate the proposed model, which predicts wind generation effectiveness for varying load profiles, wind profiles, and generation capacities. PLEXIM simulation software is used to implement a model microgrid to demonstrate the integration of wind generation and its regulatory capabilities. The proposed algorithm has applications in power system planning and operation, and it provides a method using probabilistic data set for long term energy management and optimization of islanded microgrids.

Correlation in brain networks at different time scale resolution.

Assessing brain connectivity makes up a major issue in the field of network dynamics and neuroscience. Conventional experimental techniques are based on functional imaging and magnetoencephalography, allowing to reconstruct the activity of relatively small brain volume elements. A common approach to identify networks consists in singling out sets of elements that maintain a correlated activity over time. Despite the general consensus that these networks are detectable on a time window of 10 s, no study is presently available on the distribution and thus the reliability of this time scale. In this work, we describe a new method to assess time scales on which correlations between network elements occur and to consequently identify the underlying network structures. The analysis relies on the evaluation of quasi-zero-delay cross-correlation between power sequences associated with distinct volume elements. By changing the width of the running window used to analyze successive segments of time series, the behavior of cross-correlation at different time scales was investigated. The onset of connectivity was estimated to be observable at about 30 s. The method was applied to a set of volume elements that are supposed to belong to a known resting-state network, namely the Default Mode Network. Fully connected networks were identified, provided that a sufficiently long time scale is considered. Our method makes up a new tool for the investigation of the temporal dynamics of networks.

An environmental indicator: particulate characteristics on pedestrian pathway along integrated urban thoroughfare in Metropolis

Particulate matter (PM) originated by road transport constitutes an urgent task for megacities and pedestrians are supposed to be the first batch of innocent victims that exposed to and inhaled the polluted air. Footbridges have become a promising resolution to land tension, the location and design of them should be more considered in order to provide a more desirable walking system to pedestrians. In this study, three groups of PM [i.e., 0.3–0.9 μm (sub-fine), 0.9–2.5 μm (fine) and 2.5–10 μm (coarse)] were measured at different traffic scenario related footbridges (i.e., upstream of the on-ramp, downstream of the on-ramp, and signalized intersection) along an urban artery in Hong Kong, and their traffic volume composition, multifractality and cross-correlation behavior were investigated thereafter. Multifractal detrended fluctuation analysis and multifractal detrended fluctuation cross-correlation analysis were used simultaneously to quantify the persistency of different PM groups and interaction between them. The results indicate that although the particle concentration at intersection above footbridges presents the lowest, it has the highest emission rate and the strongest multifractality and cross-correlation behavior, especially the finer ones. Hence, it is suggest that the nature ventilation style of footbridges should avoid to be built above the signalized intersection due to the long persistency of particles and active interaction between different particle groups.

MULTIFRACTAL CROSS WAVELET ANALYSIS

Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.

Cross-correlations between RMB exchange rate and international commodity markets

This paper employs multifractal detrended analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) to study cross-correlation behaviors between China’s RMB exchange rate market and four international commodity markets, using a comprehensive set of data covering the period from 22 July 2005 to 15 March 2016. Our empirical results from MF-DFA indicate that the RMB exchange rate is the most inefficient among the 4 selected markets. The results from quantitative analysis have testified the existence of cross-correlations and the result from MF-DCCA have further confirmed a strong multifractal behavior between RMB exchange rate and international commodity markets. We also demonstrate that the recent financial crisis has significant impact on the cross-correlated behavior. Through the rolling window analysis, we find that the RMB exchange rates and international commodity prices are anti-persistent cross-correlated. The main sources of multifractality in the cross-correlations are long-range correlations between RMB exchange rate and the aggregate commodity, energy and metals index.

Multiscale multifractal properties between ground-level ozone and its precursors in rural area in Hong Kong

In rural area, due to the reduction of NOx and CO emitted from vehicle exhausts, the ozone photochemical reaction exhibits relatively weak effect and ozone formation presents different pattern with its precursors in contrast to urban situation. Hence, in this study, we apply detrended cross-correlation analysis to investigate the multifractal properties between ozone and its precursors in a rural area in Hong Kong.The observed databases of ozone, NO2, NOx and CO levels during 2005–2014 are obtained from a rural monitoring station in Hong Kong. Based on the collected database, the cross-correlation analysis is carried out firstly to examine the cross-correlation patterns and the results indicate that close interactive relations exist between them. Then the detrended cross-correlation analysis is performed for further analysis. The multifractal characters occur between ozone and its precursors. The long-term cross-correlations behaviors in winter are verified to be stronger than that in other seasons. Additionally, the method is extended on daily averaged data to explore the multifractal property on various time scales. The long-term cross-correlation behavior of ozone vs NO2 and NOx on daily basis becomes weaker while that of ozone vs CO becomes stronger. The multifractal properties for all pairs in summer are found to be the strongest among the whole year. These findings successfully illustrate that the multifractal analysis is a useful tool for describing the temporal scaling behaviors of ozone trends in different time series in rural areas.