• 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

一些受新冠疫情严重影响的主要富裕国家之间不断变化的经济关系:基于股票市场视角的比较研究

The changing economic relationship between some of the major COVID-19 impacted countries with prominent wealth: a comparative study from the view point of stock markets.

作者信息

Samadder Swetadri, Ghosh Koushik

机构信息

Department of Mathematics, Fakir Chand College, South 24 Parganas, Diamond Harbour, 743331 India.

Department of Mathematics, University Institute of Technology, The University of Burdwan, Golapbag (North), Burdwan, 713104 India.

出版信息

Eur Phys J Spec Top. 2022;231(18-20):3505-3535. doi: 10.1140/epjs/s11734-022-00616-4. Epub 2022 Jun 27.

DOI:10.1140/epjs/s11734-022-00616-4
PMID:35789684
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9244491/
Abstract

In the present work, a study has been made over the prime stock indices of some fiscally prominent countries impacted by COVID-19. The countries are separated in two ways: (1) considering gross total number of infected cases-here seven mostly impacted countries with certain global economic influence are selected; (2) considering the concentration of the infected cases-here six major impacted countries with considerable influence are selected. This sort of categorization is itself a novel strategy which is capable of including some less populated, but severely impacted countries of economic importance. The objective of the present analysis is to comprehend the impact of COVID-19 on these markets and to recognize the effect of COVID-19 on mutual association and dependence between these markets. To add more flavour of reliability, we have taken a new and fresh strategy of fixing the time frames under consideration before and during COVID-19 pandemic as uniform. We have used both linear and nonlinear Granger causality analysis and employed generalized forecast error variance decomposition analysis to review the exogeneity and endogeneity of the individual markets. The present study shows that this pandemic has changed the underlying relationship: some exogenous stock markets have become endogenous and vice versa in the pandemic. Linear relationship has been reduced radically, whereas nonlinear relationship has been improved during the COVID-affected period. TASE, the highest returned and significantly uncorrelated index, emerged as the most exogenous market in the pre-COVID period, though it is nonlinearly endogenous in the long term, in the COVD-affected period. CAC 40 is the most endogenous market for the short term in both pre-COVID and COVID-affected period. B3 and NYSE, exogenous in the pre-COVID period, turned out to be linearly endogenous in the COVID-affected duration, whereas BIST 100 and BSE SENSEX are found to be exogenous markets in the COVID-affected period according to both linear and nonlinear causal analysis. They were also exogenous in the pre-COVID era for the short-term period, with BSE SENSEX exhibiting exogeneity anti-persistently for the COVID-affected period too. Association among the markets is more in long term rather than short term. A possible conclusion is also that the markets may regain long-term association once the effect of COVID would fade away.

摘要

在本研究中,我们对一些受新冠疫情影响且在财政方面较为突出的国家的主要股票指数进行了研究。这些国家通过两种方式进行划分:(1)考虑感染病例总数——在此选取了七个受影响最严重且具有一定全球经济影响力的国家;(2)考虑感染病例的集中程度——在此选取了六个受影响重大且具有相当影响力的国家。这种分类本身就是一种新颖的策略,它能够纳入一些人口较少但经济上受到严重影响的重要国家。本分析的目的是了解新冠疫情对这些市场的影响,并认识到新冠疫情对这些市场之间相互关联和依存关系的作用。为了增加可靠性,我们采用了一种全新的策略,将新冠疫情之前和期间所考虑的时间框架统一设定。我们使用了线性和非线性格兰杰因果关系分析,并采用广义预测误差方差分解分析来审视各个市场的外生性和内生性。本研究表明,这场疫情改变了潜在的关系:在疫情期间,一些外生的股票市场变成了内生市场,反之亦然。线性关系大幅减少,而在新冠疫情影响期间,非线性关系得到了改善。TASE是回报率最高且显著不相关的指数,在新冠疫情之前是最具外生性的市场,不过从长期来看它在非线性上是内生的,在新冠疫情影响期间也是如此。CAC 40在新冠疫情之前和影响期间的短期内都是最具内生性的市场。B3和纽约证券交易所(NYSE)在新冠疫情之前是外生的,但在新冠疫情影响期间变成了线性内生市场,而根据线性和非线性因果分析,伊斯坦布尔证交所100指数(BIST 100)和孟买证券交易所敏感30指数(BSE SENSEX)在新冠疫情影响期间是外生市场。它们在新冠疫情之前的短期内也是外生的,孟买证券交易所敏感30指数在新冠疫情影响期间也呈现出反持续性的外生性。市场之间的关联在长期比短期更为明显。一个可能的结论是,一旦新冠疫情的影响消退,市场可能会重新恢复长期关联。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7c/9244491/f4d58751ae7a/11734_2022_616_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7c/9244491/97bc7172a9ab/11734_2022_616_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7c/9244491/2cbdf102e15d/11734_2022_616_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7c/9244491/f4d58751ae7a/11734_2022_616_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7c/9244491/97bc7172a9ab/11734_2022_616_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7c/9244491/2cbdf102e15d/11734_2022_616_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7c/9244491/f4d58751ae7a/11734_2022_616_Fig3_HTML.jpg

相似文献

1
The changing economic relationship between some of the major COVID-19 impacted countries with prominent wealth: a comparative study from the view point of stock markets.一些受新冠疫情严重影响的主要富裕国家之间不断变化的经济关系:基于股票市场视角的比较研究
Eur Phys J Spec Top. 2022;231(18-20):3505-3535. doi: 10.1140/epjs/s11734-022-00616-4. Epub 2022 Jun 27.
2
Analysis of risk correlations among stock markets during the COVID-19 pandemic.新冠疫情期间股票市场风险相关性分析
Int Rev Financ Anal. 2022 Oct;83:102220. doi: 10.1016/j.irfa.2022.102220. Epub 2022 Jun 3.
3
Stock Market Reactions to COVID-19 Pandemic Outbreak: Quantitative Evidence from ARDL Bounds Tests and Granger Causality Analysis.新冠疫情爆发对股市的反应:来自 ARDL 边界检验和格兰杰因果分析的定量证据。
Int J Environ Res Public Health. 2020 Sep 15;17(18):6729. doi: 10.3390/ijerph17186729.
4
The dynamic causality between Chinese and ASEAN stock markets.中国与东盟股票市场之间的动态因果关系。
Heliyon. 2023 Dec 1;9(12):e22975. doi: 10.1016/j.heliyon.2023.e22975. eCollection 2023 Dec.
5
The effects of COVID-19 on the interrelationship among oil prices, stock prices and exchange rates in selected oil exporting economies.新冠疫情对部分石油出口经济体中油价、股价和汇率之间相互关系的影响。
Resour Policy. 2022 Aug;77:102744. doi: 10.1016/j.resourpol.2022.102744. Epub 2022 May 12.
6
Comovement of african stock markets: Any influence from the COVID-19 pandemic?非洲股票市场的共同变动:新冠疫情有任何影响吗?
Heliyon. 2024 Apr 23;10(9):e29409. doi: 10.1016/j.heliyon.2024.e29409. eCollection 2024 May 15.
7
Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter?新冠疫情背景下股票市场之间的动态关联性:经济政策不确定性重要吗?
Financ Innov. 2021;7(1):13. doi: 10.1186/s40854-021-00227-3. Epub 2021 Mar 1.
8
Reassessing the dynamics between exchange, oil, stock markets and uncertainty during COVID-19 in emerging market economies.重新评估新兴市场经济体在新冠疫情期间汇率、石油、股票市场与不确定性之间的动态关系。
MethodsX. 2023;10:101990. doi: 10.1016/j.mex.2022.101990. Epub 2022 Dec 28.
9
Transfer Entropy Granger Causality between News Indices and Stock Markets in U.S. and Latin America during the COVID-19 Pandemic.新冠疫情期间美国和拉丁美洲新闻指数与股票市场之间的转移熵格兰杰因果关系
Entropy (Basel). 2022 Oct 5;24(10):1420. doi: 10.3390/e24101420.
10
Emerging stock market reactions to shocks during various crisis periods.新兴市场对各种危机期间冲击的反应。
PLoS One. 2022 Sep 13;17(9):e0272450. doi: 10.1371/journal.pone.0272450. eCollection 2022.

本文引用的文献

1
The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model.新冠疫情对七国集团股票市场波动性的影响:来自ST-HAR模型的证据。
Int Rev Financ Anal. 2021 Mar;74:101671. doi: 10.1016/j.irfa.2021.101671. Epub 2021 Jan 13.
2
Will the COVID-19 pandemic end with the Delta and Omicron variants?新冠疫情会随着德尔塔和奥密克戎变种而结束吗?
Environ Chem Lett. 2022;20(4):2215-2225. doi: 10.1007/s10311-021-01369-7. Epub 2022 Jan 15.
3
Omicron: a mysterious variant of concern.奥密克戎:一种令人担忧的神秘变体。
Eur Phys J Plus. 2022;137(1):100. doi: 10.1140/epjp/s13360-021-02321-y. Epub 2022 Jan 10.
4
Sequence analysis of the emerging SARS-CoV-2 variant Omicron in South Africa.南非出现的 SARS-CoV-2 变异株奥密克戎的序列分析。
J Med Virol. 2022 Apr;94(4):1728-1733. doi: 10.1002/jmv.27516. Epub 2021 Dec 27.
5
An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion.基于分形的新冠疫情第二波传播预后模型探索与比较分析
Nonlinear Dyn. 2021;106(2):1375-1395. doi: 10.1007/s11071-021-06865-7. Epub 2021 Sep 8.
6
Financial Return Distributions: Past, Present, and COVID-19.财务回报分布:过去、现在与新冠疫情
Entropy (Basel). 2021 Jul 12;23(7):884. doi: 10.3390/e23070884.
7
How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques.新冠疫情如何推动商品市场与金融市场之间的关联性:来自时变参数向量自回归模型(TVP-VAR)和分位数因果关系技术的证据
Resour Policy. 2021 Mar;70:101898. doi: 10.1016/j.resourpol.2020.101898. Epub 2020 Oct 20.
8
The second and third waves in India: when will the pandemic be culminated?印度的第二波和第三波疫情:疫情何时会达到顶峰?
Eur Phys J Plus. 2021;136(5):596. doi: 10.1140/epjp/s13360-021-01586-7. Epub 2021 May 28.
9
Nonlinear science against the COVID-19 pandemic.应对新冠疫情的非线性科学
Physica D. 2021 Oct;424:132946. doi: 10.1016/j.physd.2021.132946. Epub 2021 Apr 30.
10
Explicit formulae for the peak time of an epidemic from the SIR model.基于SIR模型的传染病流行高峰时间的显式公式。
Physica D. 2021 Aug;422:132902. doi: 10.1016/j.physd.2021.132902. Epub 2021 Mar 26.