Dynamic relationship between stock market trading volumes and investor fear gauges movements

This article applies multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) to investigate cross-correlation behaviours between two kinds of stock markets trading volumes and investor fear gauges covering the data of U.S. stock markets from 2 January 2004 to 31 July 2018. The empirical results show that the dynamic relationship between stock markets trading volume fluctuations and different kinds of investor fear gauges are multifractal and find that the dynamic relationship is strongly anti-persistent. Moreover, financial crisis in 2008 has a significant impact on the cross-correlated behaviour, suggesting that stock market trading volume fluctuations and investor fear gauges are more susceptible to each other during the financial crisis period. Through the rolling windows analysis, we also find that the stock markets trading volume fluctuations and different kinds of investor fear gauges are anti-persistent dynamic cross-correlated.     

基于MF-DCCA方法的证券市场间交叉相关性研究

运用多重分形去趋势波动交叉相关分析法(MF-DCCA),考量上海证券市场和香港证券市场之间的交叉相关关系。实证表明：上海证券市场和香港证券市场之间存在交叉相关性,且呈现出多重分形特征;当证券市场出现较大的波动时,上海证券市场和香港证券市场的交叉标度指数要大于其平均标度指数,即两个证券市场之间的交叉相关性要大于其自相关性。     

互联网货币基金的多重分形分析——以余额宝、理财通为例

本文以余额宝、理财通为互联网货币基金代表,选取两种货币基金2013-2020年的七日年化收益率数据为研究区间,运用多重分形分析方法研究了两种货币基金的对数日收益率序列的多标度行为及其之间的非线性动态关系。研究结果表明,余额宝与理财通两收益率序列均存在时变的多重分形特征。比较而言,理财通收益率序列的多重分形强度更大,而余额宝收益率序列的正持续性更强;在小波动下,两收益率序列均表现出强持续性;两种互联网货币基金之间的交互相关关系是正相关的,具有多重分形性。同时,讨论了新冠肺炎疫情对互联网货币基金收益率的影响,并针对当前新冠肺炎疫情形势,给出投资理财建议。     

Multifractal detrended cross-correlations between WTI crude oil price fluctuations and investor fear gauges

This article investigates the cross-correlations between WTI crude oil prices and fear gauges using cross-correlation statistic test and multifractal detrended cross-correlation analysis. The results show that the cross-correlations between crude oil prices and three different kinds of fear gauges are multifractal. By finding the ‘crossover’, we separate the three pairs of time series into the short term and long term, and find that cross-correlations of small fluctuations are persistent in the short and long terms, cross-correlations of large fluctuations are strongly anti-persistent in the short and long terms. The relationship is useful to profit in future markets.