Asymmetric Detrended Cross-correlation Analysis Research Articles

Exploring the Cross-Correlations between Tesla Stock Price, New Energy Vehicles and Oil Prices: A Multifractal and Causality Analysis

The interaction between new energy vehicle (NEV) stock prices and the crude oil market is crucial for resource allocation and risk management. This study employs Multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the multifractal characteristics of the cross-correlation between Tesla stock price (TSLA) and crude oil price (Brent), as well as between TSLA and other NEV stocks (excluding TSLA). The experimental results reveal long-term persistence and multiple fractal characteristics in the cross-correlations. Additionally, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) demonstrates the asymmetry of the cross-correlation during upward or downward trends between TSLA and Brent, as well as between TSLA and other NEV stocks (excluding TSLA). Furthermore, utilizing the transfer entropy (TE) method, we assess the strength and direction of information flows between TSLA and Brent, and between TSLA and other NEV stocks (excluding TSLA). Interestingly, we observe bidirectional information transmission between TSLA and other NEV stocks, while only unidirectional information transmission from NIO to TSLA is evident. These findings provide valuable insights for resource allocation, supply chain management and sustainable development strategies for decision-makers in the NEV market.

Interplay Multifractal Dynamics Among Carbon Trading Market, Geopolitical Risk and Economic Policy Uncertainty

This paper explores the variations in cross-correlations among the carbon trading market, geopolitical risk (GPR) and economic policy uncertainty (EPU), focusing on their multifractality and asymmetric properties. Therefore, the study employs the multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) approaches to examine these relationships from a three-dimensional perspective. Also, the study looks into cross-correlations at different phases of carbon trading. Our findings reveal that the multifractal cross-correlations between the carbon trading market and GPR or EPU exhibit anti-persistence and asymmetry. The anti-persistent cross-correlations are notably stronger when GPR experiences a downtrend or when EPU undergoes an uptrend. Furthermore, distinct fluctuation patterns emerge across various trends and scales on Hurst surfaces. In the short term, the carbon trading market exhibits heightened sensitivity to changes in GPR or EPU when they rise or fall. The cross-correlations remain multifractal, anti-persistent and asymmetric across different stages of carbon trading. Notably, the multifractality of cross-correlations is most pronounced for the series pair EUA/GPR in Phase II and for the series pair EUA/UK EPU in Phase III and Phase IV. Except for the series pair EUA/UK EPU in Phase II, the anti-persistent cross-correlations are more pronounced during uptrend than downtrend for the other series pairs. Moreover, the series pair EUA/UK EPU exhibits the highest degree of asymmetry at all stages.

Exploring the Asymmetric Multifractal Characteristics of Price–Volume Cross-Correlation in the Chinese Rebar Futures Market Based on MF-ADCCA

In this paper, we employ multifractal detrended cross-correlation analysis (MF-DCCA) to study the cross-correlation between closing price and trading volume and the multifractal characteristics of the relationship. The results show that the price, volume and price–volume cross-correlation in the rebar futures market have significant multifractal features. We find that the multifractal characteristics of price–volume cross-correlation are derived from long-term correlation and fat-tailed distribution. However, MF-DCCA cannot be used to explore the price–volume cross-correlation under different market trends (upwards or downwards). Therefore, we further adopt multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) to explore the asymmetric multifractal characteristics of price–volume cross-correlation. The empirical results show that the price–volume cross-correlation in the rebar futures market has significant asymmetric multifractality. Specifically, the asymmetry of the price–volume cross-correlation is more robust for both the price changes and the volume changes in a downtrend than that in an uptrend, indicating that the downward market tends to be more complex and riskier than the upward market in Shanghai Futures Exchange (SHFE) rebar futures market. Therefore, investors should pay extra attention to the downward market of the rebar futures and make reasonable investment strategies according to their risk appetite.

Analysis of Risk Spillover and Asymmetry Between Three Crude Oil Markets and Chinese Financial Markets

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China’s future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations.

Stock Index Return Volatility Forecast via Excitatory and Inhibitory Neuronal Synapse Unit with Modified MF-ADCCA

Financial prediction persists a strenuous task in Fintech research. This paper introduces a multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA)-based deep learning forecasting model to predict a succeeding day log return via excitatory and inhibitory neuronal synapse unit (EINS) using asymmetric Hurst exponent as input features, with return and volatility increment of Shanghai Stock Exchanges Composite Index (SSECI) from 2014 to 2020 as proxies for analysis. Experimental results revealed that multifractal elements by MF-ADCCA method as input features are applicable to time series forecasting in deep learning than multifractal detrended fluctuation analysis (MF-DFA) method. Further, the proposed biologically inspired EINS model achieved satisfactory performances in effectiveness and reliability in time series prediction compared with prevalent recurrent neural networks (RNNs) such as LSTM and GRU. The contributions of this paper are to (1) introduce a moving-window MF-ADCCA method to obtain asymmetric Hurst exponent sequences used directly as an input feature for deep learning prediction and (2) evaluate performances of various asymmetric multifractal approaches for deep learning time series forecasting.

Comparison of Price-Volume Correlation for Some Cryptocurrencies Based on MF-ADCCA

In this paper, we explored the price-volume cross-correlation asymmetric multifractal properties of some cryptocurrencies using multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). Bitcoin, Ethereum, Tether, Binance Coin, Cardano, and Dogecoin are examples of cryptocurrencies. The empirical results reveal that the price-volume cross-correlations of all six cryptocurrencies display anti-persistent, multifractal, and asymmetric features, although the degree of these characteristics differs depending on the cryptocurrencies and market trends. Specifically, the anti-persistence multifractal of the price-volume cross-correlation is significantly stronger in the upward market than in the downward market for Bitcoin and Ethereum; for the remaining four cryptocurrencies, the opposite result is obtained. Furthermore, Dogecoin price-volume cross-correlation exhibits the most marked anti-persistence and the most significant asymmetric multifractality among the six cryptocurrencies analyzed.

Asymmetric Multifractal Cross-Correlations Between Economic Policy Uncertainty and Agricultural Futures Prices

This paper investigates the fluctuation characteristics and asymmetry of cross-correlations between economic policy uncertainty (EPU) and agricultural futures prices in China and the US by using the multifractal detrended cross-correlation analysis (MF-X-DFA) and multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). We find that the multifractal cross-correlations exist between EPU and agricultural futures prices, and the cross-correlations are anti-persistent and asymmetric. The anti-persistent cross-correlations in China are all stronger than those in the US. The multifractal degree of cross-correlation between EPU and soybean futures price is lower in China than in the US, while the multifractal degree of cross-correlation between EPU and soybean meal, soybean oil or corn futures price is higher in China than in the US. Moreover, China’s soybean futures price is more susceptible to the upward and downward trends in China’s EPU, while the US soybean meal, soybean oil, and corn futures prices are more susceptible to them in the US EPU.

The Cross-Correlation Relationship Between the Real Estate Industry and High-Quality Economic Development

Abstract Based on China’s real estate investment (REI), commercial housing sales (CHS), and GDP data from 2001 to 2020, we studied the dependence, asymmetry, and transmission direction of China’s real estate and economy. Their cross-correlation is demonstrated through the deep cross-correlation analysis (DCCA) algorithm, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) algorithm and DCCA algorithm based on a time delay. Both REI and CHS have persistence with GDP. The real estate industry and economy have asymmetric multifractal characteristics. Meanwhile, real estate and the economy have different trends, and they can affect each other with the same length of time delay. This effect diminishes in a short period of time, and there are unsteady fluctuations in cross-correlation persistence in the long term.

Asymmetric multifractal features of the price–volume correlation in China’s gold futures market based on MF-ADCCA

In recent years, fractal theory has become a recognized research direction for explaining various complex phenomena that are difficult to constrain in the conventional efficient market hypothesis for financial markets. Moreover, because the gold futures prices are crucial to the futures market, research on the relationship between quantity and price is important for understanding market fluctuations. Therefore, this paper conducts an empirical analysis of the multifractal features and asymmetry in the price–volume correlation of China’s gold futures market based on the multifractal asymmetric detrended cross-correlation analysis method11Multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method. . Results show that the cross-correlation between market price and volume is asymmetric and multifractal and that multifractal features are stronger when the price increases compared with when it declines. Moreover, the multifractal features vary over time. These findings indicate that the risk of China’s gold futures market will change with the price trend over time.

Asymmetric multi-fractal cross-correlations of the price of electricity in the US with crude oil and the natural gas

The price of commodities are often difficult to forecast due to the underlying characteristics of the series such as autocorrelation, heteroskedasticity, and non-linearity. For many commodities, it is both socially and financially detrimental to have too much error in the forecasts. In the case of electricity production, both stock-outs and over production result in failure in the form of blackouts on one side and waste on the other. Therefore, it is desirable to improve the models that are used to forecast both the production and demand of electricity in order to optimize the match between supply and demand. To accomplish the aforementioned, understanding of the behavior of the commodity is necessary. In this paper, we apply a multi-fractal asymmetric detrended cross-correlation analysis to analyze the presence and asymmetry of the cross-correlations between the price of electricity in U.S. with respect to crude oil and natural gas markets. Our data draws from the major producers of electricity in the U.S. and evaluates the multi-fractal asymmetric detrended cross-correlation with respect to WTI and Natural Gas; both important inputs into the production of electricity. Our findings illustrate the fractal and cross-correlation relationship between electricity production and commodity prices.

Cross-correlations between price and volume in China's crude oil futures market: A study based on multifractal approaches

Abstract In this paper, we investigate the multifractal characteristics of the cross-correlations between price and volume in China's first crude oil futures market (INE), and compared the INE market with Brent and WTI market. Multifractal detrended cross-correlation analysis (MF-DCCA) was applied to comprehensively research the multifractal characteristics of the three different crude oil futures markets based on the data of the past five years. The results show that the cross-correlations between price change and volume change have significant multifractal characteristics in three markets, and the degree of multifractality is stronger in the WTI market for the full sample. Due to the INE market appeared late, the sample size of INE is not consistent with that of the other two markets. In order to solve this influence, we further compare the multifractal characteristics of the three crude oil futures markets in the same period (March 26, 2018 to November 20, 2020) and compares the results with those of the full sample period. The empirical results show that the degree of multifractality in both Brent and WTI markets is increased. To further verify whether this result is due to the emergence of the INE crude oil market, the entire sample of the Brent and WTI markets are divided into two periods according to the launch time of INE market. The result shows that the degree of multifractality and the risk of these two markets are all stronger in the last two years. Thus, there are reasons to believe that the emergence of INE market has a certain impact on the traditional Brent and WTI market, and a greater impact on Brent market. However, MFDCCA only consider the overall situation and cannot describe the multifractal cross-correlation between the upward and downward trends of the market. Thus, it probably won't accurately reflect the nonlinear cross-correlation. Furthermore, we study the characteristics of the asymmetric multifractal cross-correlations in different trends (upwards or downwards) by using the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method. Empirical results indicate that the cross-correlation between price and volume in three markets shows obvious asymmetry. Specifically, when the price changes series is on an upward trend, or when the volume changes series is on a downward trend, the degree of asymmetry in three markets is stronger, and the risk situation of the market is more complex. Therefore, for a portfolio of assets with different markets, investors should fully observe and understand different market trends and make reasonable investment based on their own risk preference.

Multifractal Fluctuation Analysis of Correlations Between the Sector Stock Markets in China and the US

With the rapid development of economic globalization, the stock markets in China and the US are increasingly linked. The fluctuation features and cross-correlations of the two countries’ markets have attracted extensive attention from market investors and researchers. In this paper, the fractal analysis methods including multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) and coupled detrended cross-correlation analysis (CDCCA) are applied to explore the volatilities of CSI300 and SP500 sector stock indexes as well as the cross-correlations and coupling cross-correlations between the two corresponding sector stock indexes. The results show that the auto-correlations, cross-correlations and coupling cross-correlations have multifractal fluctuation characteristics, and that the cross-correlations are asymmetric. Additionally, the coupling cross-correlation strengths are distinct due to the different influence of long-range correlations and fat-tailed distribution. Further, the co-movement between China and the US sector stock markets is susceptible to external market factors such as major economic events and national policies.

Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis

We study the asymmetric multifractality between five main cryptocurrencies (Bitcoin, Litecoin, Ripple, Monero, and Dash) and six equity ETFs from February 2, 2015 to April 30, 2019. The equity ETFs selected relate to emerging markets, China, Japan, the energy sector, financial sector, and technology–Nasdaq. Results from the multifractal asymmetric detrended cross-correlation analysis show a significant persistence and evidence of asymmetric multifractality in the cross-correlation between most of the pairs of cryptocurrencies and ETFs. These findings, which are consistent with previous findings on the susceptibility of Bitcoin to multifractality, indicate the presence of heterogeneity in the cross-relationship between most cryptocurrencies and equity ETFs.

Financial stability and real estate price fluctuation in China

The relationship between financial stability and the real estate market has received increasing attention. Understanding the interaction between them may monitor financial risk and real estate price fluctuation. This paper aims at examining the cross-correlation between financial stability and real estate price fluctuation in China. A measure of financial stability is constructed and the cross-correlation between financial stability and the real estate market is demonstrated by using detrended cross-correlation analysis (DCCA). Furthermore, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) is used to evaluate the scaling properties of the cross-correlation among financial stability and real estate price fluctuation. Empirical results indicate that asymmetries exist in the cross-correlation and that the asymmetric cross-correlation is multifractal. The cross-correlation is related to the fluctuation magnitude if the financial stress index (or the growth rate in house prices) is in a different trend. Finally, DCCA analysis based on time-delay is employed to investigate transmission direction of the cross-correlation. Our findings indicate the transmission direction is bidirectional, but the impact of financial stability on real estate price fluctuation is larger than the impact in the reverse direction.

Tracking the relationship between euro area equities and sovereign bonds

This paper explores the relationship between stocks and sovereign bonds by means of the asymmetric detrended cross-correlation analysis (ADCCA). Drawing on data from 1999.01 to 2018.09 of the first wave of euro area countries, the full sample is divided into three subsets in accordance with economic and financial features. Some findings arise with striking implications for investors and policymakers. Firstly, empirical results show that cross-correlations differ from country to country, depending on the sub-period under analysis and on the time scale. Secondly, likewise within country estimates, cross-country linkages may point to fragmentation in the euro area with agents moving away from financial assets of lower-rated countries to invest in more robust economies in periods of turmoil. Thirdly, there is evidence of asymmetry since 'flight-to-quality' movements seem to be more relevant than 'flight-to-yield' episodes. Finally, while relationships were globally bidirectional until mid-2007, new causality patterns arose with the financial crisis.

A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis.

In order to detect and quantify asymmetry of two time series, a novel cross-correlation coefficient is proposed based on recent asymmetric detrended cross-correlation analysis (A-DXA), which we called A-DXA coefficient. The A-DXA coefficient, as an important extension of DXA coefficient ρDXA, contains two directional asymmetric cross-correlated indexes, describing upwards and downwards asymmetric cross-correlations, respectively. By using the information of directional covariance function of two time series and directional variance function of each series itself instead of power-law between the covariance function and time scale, the proposed A-DXA coefficient can well detect asymmetry between the two series no matter whether the cross-correlation is significant or not. By means of the proposed A-DXA coefficient conducted over the asymmetry for California electricity market, we found that the asymmetry between the prices and loads is not significant for daily average data in 1999 yr market (before electricity crisis) but extremely significant for those in 2000 yr market (during the crisis). To further uncover the difference of asymmetry between the years 1999 and 2000, a modified H statistic (MH) and ΔMH statistic are proposed. One of the present contributions is that the high MH values calculated for hourly data exist in majority months in 2000 market. Another important conclusion is that the cross-correlation with downwards dominates over the whole 1999 yr in contrast to the cross-correlation with upwards dominates over the 2000 yr.

ASYMMETRIC-STRUCTURE ANALYSIS OF CARBON AND ENERGY MARKETS

This study aimed to investigate the asymmetric structure between the carbon and energy markets from two aspects of different trends (up or down) and volatility-transmission direction using asymmetric detrended cross-correlation analysis (DCCA) cross-correlation coefficient test, multifractal asymmetric DCCA (MF-ADCCA) method, asymmetric volatility-constrained correlation metric and time rate of information-flow approach. We sampled 1283 observations from January 2008 to December 2012 among pairs of carbon and energy markets for analysis. Empirical results show that the (1) asymmetric characteristic from the cross-correlation between carbon and returns in the energy markets is significant, (2) asymmetric cross-correlation between carbon and energy market price returns is persistent and multifractral and (3) volatility of the base assets of energy market returns is more influential to the base asset of the carbon market than that of the energy market.

Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors

PM2.5 pollution has become one of the most serious air pollution in China. The cross-correlations between PM2.5 concentration and meteorological factors (i.e., temperature, air pressure, relative humidity and wind speed) in Beijing and Hong Kong are discussed in this paper. We use the multifractal detrended cross-correlation analysis (MF-DCCA) to analyze the cross-correlations, and study further the asymmetric characteristics of cross-correlations by multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). The experimental results show that the cross-correlations between PM2.5 concentration and four meteorological factors are multifractal and anti-persistent, and the strength of multifractality of Beijing is stronger than that of Hong Kong. Meanwhile, the cross-correlations between PM2.5 concentration and meteorological factors are asymmetric, and the asymmetric cross-correlations are multifractal.

Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market

We propose a new method called the multifractal asymmetric detrended cross-correlation analysis method (MF-ADCCA) to investigate the asymmetric cross-correlations in nonstationary time series that combine the multifractal detrended cross-correlation analysis (MF-DCCA) and asymmetric detrended fluctuation analysis (A-DFA). The study aims to determine whether different scaling properties of the cross-correlations are obtained if a one-time series trending is either positive or negative. We apply MF-ADCCA to analyze empirically the scaling behavior of the cross-correlations among the Chinese stock market, the RMB exchange market, and the US stock market. Empirical results indicate that the cross-correlations between the Chinese stock market and the RMB/USD exchange market are more persistent when any one of the markets is falling. On the contrary, the cross-correlations between the Chinese stock market and the RMB/EU, RMB/GBP, RMB/JPY exchange markets and the US stock market are more persistent when one of the markets is rising. Moreover, asymmetric cross-correlations between any two of the selected financial markets are multifractal.