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.     

Why cryptocurrency markets are inefficient: The impact of liquidity and volatility

In this research, we study the multifractality, long-memory process, and efficiency hypothesis of six major cryptocurrencies (Bitcoin, Ethereum, Monero, Dash, Litecoin, and Ripple) using the time-rolling MF-DFA approach. For an in-depth analysis, this study uses the quantile regression approach to examine the determinants of efficient markets. The results show that all markets present evidence of long-memory property and multifractality. Furthermore, the inefficiency of cryptocurrency markets is time-varying, and Dash is the least inefficient market while Litecoin is the most inefficient. Finally, we find that higher liquidity improves but higher volatility weakens the efficiency of cryptocurrencies, depending on the quantiles. Therefore, we conclude that high liquidity with low volatility helps active traders to arbitrage away opportunities, resulting in market efficiency.     

中日黄金期货市场多重分形实证研究——基于OSW—MF—DFA方法

运用重叠平滑窗技术对广泛使用的多重分形去趋势波动分析（MF—DFA）进行改进,形成基于重叠平滑窗的多重分形去趋势波动分析（OSW—MF-DFA）方法。在此基础上,结合多重分形谱方法,对上海和东京的期金市场进行多重分形比较研究。结果表明,两市场均存在多重分形,且都是由日对数收益率序列的长程相关和厚尾概率分布引起。此外还发现,与上海期金市场相比,东京期金市场的分形强度更大,从而市场风险也更大,但其获利机会却与此不相匹配。     

基于MF-DFA的中国股指期货价格的多重分形实证研究

本文通过应用多重分形谱分析法和多重分形消除趋势波动分析（MF-DFA）法,研究了新产生的中国股指期货市场的多重分形性。通过对2942个股指期货最后十分钟结算价格的分析,我们发现中国股指期货的收益率具有长程相关性和多重分形性,期货价格波动并不能用单一的标度指数进行充分描述。进一步通过将原始序列和转换后的收益序列进行比较,转换过程包括重排以及相位随机化,我们发现导致中国股指期货市场多重分形性的两种不同成因。研究结果表明,虽然厚尾分布是造成多重分形性的一个方面,但长程相关性才是引起中国股指期货市场多重分形的主要原因。     

Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach

This study examines the asymmetric multifractality and the market efficiency of the stock markets in the countries that are the top crude oil producers (USA, KSA, Canada and Russia) and consumers (Brazil, China, India, and Japan) using an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method. The results show evidence of an asymmetric multifractal nature for all markets. Moreover, the multifractality is stronger in the upward movement of the market returns, except in China. The degree of efficiency of the stock markets is shown to be time-varying and experienced a decrease during the 2008 global financial crisis (GFC), but an upside trend occurred during the recent oil price crash followed a significant decline during COVID-19. The stock markets have an anti-persistent feature during GFC and COVID-19, whereas they exhibit a long-term persistent feature during oil price crash. More interestingly, the efficiency of the stock markets of crude oil producers is lower in general than that of oil consumers. Furthermore, the efficiency of the stock market is lower in the downward movement of the market returns than in the upward movement. Asymmetry and oil price uncertainty index are the key driver of the stock markets and can serve as predictor of the stock market dynamics of top oil producers and top oil consumers particularly during COVID-19 and oil price crash.     

基于MF-DFA的股票时间序列聚类分析及其应用

多重分形消除趋势波动分析法(MF-DFA)不仅能够去除股票时间序列的长期趋势波动,还能够精确反应股票时间序列的多重分形特性。首先利用MF-DFA方法对股票时间序列进行多重分形分析,结果表明,相比标准多重分析,MF-DFA方法更能反映时间序列的多重分形特性。其次,定义一种以多重分形谱参数作为相似性度量函数的聚类方法对股票时间序列进行聚类。最后,在Markowitz提出的"期望均值收益—收益方差"(M-V)模型的基础上,把聚类结果运用股票投资组合当中。采用上海证券市场28支股票进行实验验证表明,在给定的收益率下,采用基于多重分形谱参数的聚类方法的股票组合可以得到比随机组合更小的风险水平。     

股指时间序列的分形分析及预测

以沪深300指数作为股指时间序列的研究对象,首先运用分形插值法对股指序列的运行规律及波动特征进行分析和预测,并使用分形维数与Hurst指数定量刻画股指序列的波动复杂性及长程相关性。然后运用MF-DFA以及多重分形谱分析法,对股指序列的收益率作进一步的分析,结果表明,股指收益率序列具有多重分形性,并呈现出状态持续性或反持续性等特征。