• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于MF-DCCA模型的中美股票市场互相关分析与多重分形分析

Cross-correlation and multifractality analysis of the Chinese and American stock markets based on the MF-DCCA model.

作者信息

Chen Yijun, Zhang Jun-Hao, Lu Lei, Xie Zi-Miao

机构信息

College of Finance, Guizhou University of Commerce, Avenida 26, 550014, Guiyang, PR China.

School of Business, Macau University of Science and Technology, Avenida Wailong, Taipa, 999078, Macao, PR China.

出版信息

Heliyon. 2024 Aug 22;10(17):e36537. doi: 10.1016/j.heliyon.2024.e36537. eCollection 2024 Sep 15.

DOI:10.1016/j.heliyon.2024.e36537
PMID:39281645
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11400966/
Abstract

OBJECTIVE

To compare the multifractal features and factors of the Chinese and American stock markets and their correlation, complexity and uncertainty.

METHODS

The paper analyzes the CSI 300 and S&P 500 indices from March 2018 to March 2023 using the MF-DCCA model and removes the long-term memory and nonlinear effects by random reshuffling and phase processing methods.

RESULTS

The paper shows that (1) CSI 300 and S&P 500 have multifractal features, with different long-term memory, complexity and irregularity at different scales; (2) The markets are fractal movements influenced by investors' irrationality and expectations, not efficient markets; (3) Long-term memory and nonlinear effects cause the multifractal features. The paper offers a new perspective and method for the market investors and regulators.

摘要

目的

比较中美股票市场的多重分形特征、影响因素及其相关性、复杂性和不确定性。

方法

本文运用多重分形去趋势波动分析(MF-DCCA)模型,对2018年3月至2023年3月的沪深300指数和标准普尔500指数进行分析,并通过随机重排和相位处理方法消除长期记忆和非线性效应。

结果

研究表明:(1)沪深300指数和标准普尔500指数具有多重分形特征,在不同尺度上具有不同的长期记忆、复杂性和不规则性;(2)市场是受投资者非理性和预期影响的分形运动,而非有效市场;(3)长期记忆和非线性效应导致了多重分形特征。本文为市场投资者和监管者提供了新的视角和方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/8a4515f7d986/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/9876024a51d1/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/800a6de6a255/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/27ae19265022/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/8a4515f7d986/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/9876024a51d1/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/800a6de6a255/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/27ae19265022/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90d5/11400966/8a4515f7d986/gr4.jpg

相似文献

1
Cross-correlation and multifractality analysis of the Chinese and American stock markets based on the MF-DCCA model.基于MF-DCCA模型的中美股票市场互相关分析与多重分形分析
Heliyon. 2024 Aug 22;10(17):e36537. doi: 10.1016/j.heliyon.2024.e36537. eCollection 2024 Sep 15.
2
Nonlinear relationship between money market rate and stock market liquidity in China: A multifractal analysis.中国货币市场利率与股票市场流动性之间的非线性关系:多分形分析。
PLoS One. 2021 Apr 16;16(4):e0249852. doi: 10.1371/journal.pone.0249852. eCollection 2021.
3
Interplay of multifractal dynamics between shadow policy rates and stock markets.影子政策利率与股票市场之间的多重分形动力学相互作用。
Heliyon. 2023 Jul 10;9(7):e18114. doi: 10.1016/j.heliyon.2023.e18114. eCollection 2023 Jul.
4
Multifractal Behaviors of Stock Indices and Their Ability to Improve Forecasting in a Volatility Clustering Period.股票指数的多重分形行为及其在波动聚类时期改善预测的能力。
Entropy (Basel). 2021 Aug 6;23(8):1018. doi: 10.3390/e23081018.
5
Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis.中国新能源市场的市场效率及其与其他资产的交叉相关性:基于多重分形分析的证据
Comput Econ. 2022 Aug 11:1-25. doi: 10.1007/s10614-022-10301-2.
6
On the inner dynamics between Fossil fuels and the carbon market: a combination of seasonal-trend decomposition and multifractal cross-correlation analysis.化石燃料与碳市场的内在动态关系:季节趋势分解与多重分形交叉相关性分析的结合。
Environ Sci Pollut Res Int. 2023 Feb;30(10):25873-25891. doi: 10.1007/s11356-022-23924-7. Epub 2022 Nov 9.
7
Skewed multifractal scaling of stock markets during the COVID-19 pandemic.新冠疫情期间股票市场的偏态多重分形标度
Chaos Solitons Fractals. 2023 May;170:113372. doi: 10.1016/j.chaos.2023.113372. Epub 2023 Mar 21.
8
Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices.风格股票指数的非对称分形特征与市场效率分析
Entropy (Basel). 2022 Jul 13;24(7):969. doi: 10.3390/e24070969.
9
A New Look at Calendar Anomalies: Multifractality and Day-of-the-Week Effect.日历异常现象新探:多重分形与星期效应
Entropy (Basel). 2022 Apr 17;24(4):562. doi: 10.3390/e24040562.
10
Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets.用于量化幂律交叉相关性的多重分形时间加权去趋势交叉相关性分析及其在股票市场中的应用。
Chaos. 2017 Jun;27(6):063111. doi: 10.1063/1.4985637.

本文引用的文献

1
Multifractal analysis of financial markets: a review.金融市场的多重分形分析:综述
Rep Prog Phys. 2019 Dec;82(12):125901. doi: 10.1088/1361-6633/ab42fb. Epub 2019 Sep 10.
2
Statistical tests for power-law cross-correlated processes.幂律交叉相关过程的统计检验。
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Dec;84(6 Pt 2):066118. doi: 10.1103/PhysRevE.84.066118. Epub 2011 Dec 22.
3
Multifractal detrended cross-correlation analysis for two nonstationary signals.用于两个非平稳信号的多重分形去趋势互相关分析
Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Jun;77(6 Pt 2):066211. doi: 10.1103/PhysRevE.77.066211. Epub 2008 Jun 18.
4
Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series.去趋势交叉相关性分析:一种分析两个非平稳时间序列的新方法。
Phys Rev Lett. 2008 Feb 29;100(8):084102. doi: 10.1103/PhysRevLett.100.084102. Epub 2008 Feb 27.