An empirical test on long memory between price and trading volume of China metals futures market was given with MF-DCCA method. The empirical results show that long memory feature with a certain period exists in price...An empirical test on long memory between price and trading volume of China metals futures market was given with MF-DCCA method. The empirical results show that long memory feature with a certain period exists in price-volume correlation and a fittther proof was given by analyzing the source of multifractal feature. The empirical results suggest that it is of important practical significance to bring the fractal market theory and other nonlinear theory into the analysis and explanation of the behavior in metal futures market.展开更多
The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the B...The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.展开更多
In this paper, we select yield series of the SSE index and the S & P 500 index as the research object. Firstly, we take the financial crisis as the dividing point, and decompose the whole sample period into three ...In this paper, we select yield series of the SSE index and the S & P 500 index as the research object. Firstly, we take the financial crisis as the dividing point, and decompose the whole sample period into three periods: before the financial crisis, during the financial crisis, and after the financial crisis. Secondly, the degree of interaction between Chinese and American stock markets was tested and calculated in stages, and the cross-correlation relationship became more significant after the financial crisis. Then the MF-DCCA method is used to analyze the multifractal interaction of the whole period. It is found that the interaction relationship is multifractal in the short-term and long-term, and shows stronger in the short-term. In addition, the interaction relationship is persistent for small fluctuations in the short-term, and it is anti-sustainability in the case of large fluctuations;it is persistent in all fluctuations in the long run. Finally, the multifractal analysis was carried out for the three periods. It was found that during the financial crisis, the interaction had stronger multifractality and volatility, and the risk was higher.展开更多
基金Project(13&ZD024)supported by the Major Program of the National Social Science Fund of ChinaProject(71073177)supported by the National Natural Science Foundation of China+3 种基金Project(CX2012B107)supported by the Graduate Student Innovation Project of Hunan Province,ChinaProject(13YJAZH149)supported by the Social Science Fund of Ministry of Education of ChinaProject(2011ZK2043)supported by the Key Program of the Soft Science Research Project of Hunan Province,ChinaProject(12JJ4077)supported by Natural Science Foundation of Hunan Province of China
文摘An empirical test on long memory between price and trading volume of China metals futures market was given with MF-DCCA method. The empirical results show that long memory feature with a certain period exists in price-volume correlation and a fittther proof was given by analyzing the source of multifractal feature. The empirical results suggest that it is of important practical significance to bring the fractal market theory and other nonlinear theory into the analysis and explanation of the behavior in metal futures market.
基金supported by the National Natural Science Foundation of China (42230708)the Joint CAS (Chinese Academy of Sciences) & MPG (Max-Planck-Gesellschaft) Research Project (HZXM20225001MI)the Tianshan Talent Project of Xinjiang Uygur Autonomous Region, China (2022TSYCLJ0056)。
文摘The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.
文摘In this paper, we select yield series of the SSE index and the S & P 500 index as the research object. Firstly, we take the financial crisis as the dividing point, and decompose the whole sample period into three periods: before the financial crisis, during the financial crisis, and after the financial crisis. Secondly, the degree of interaction between Chinese and American stock markets was tested and calculated in stages, and the cross-correlation relationship became more significant after the financial crisis. Then the MF-DCCA method is used to analyze the multifractal interaction of the whole period. It is found that the interaction relationship is multifractal in the short-term and long-term, and shows stronger in the short-term. In addition, the interaction relationship is persistent for small fluctuations in the short-term, and it is anti-sustainability in the case of large fluctuations;it is persistent in all fluctuations in the long run. Finally, the multifractal analysis was carried out for the three periods. It was found that during the financial crisis, the interaction had stronger multifractality and volatility, and the risk was higher.
