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本项目主要是对2008年1月1日-2021年12月31日我国1343家非金融企业的系统性风险进行测度并对风险传染机制进行分析,其主要内容包含以下两个部分:(1)基于DCC-GARCH模型的系统性风险(MES)测度,(2)复杂网络的抗毁性分析

R 0.10% Jupyter Notebook 99.90%

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2022-statistical-modeling

本项目主要是对2008年1月1日-2021年12月31日我国1343家非金融企业的系统性风险进行测度并对风险传染机制进行分析,其主要内容包含以下两个部分:
(1)基于DCC-GARCH模型的系统性风险(MES)测度;(2)复杂网络的抗毁性分析。
其中系统性风险(MES)测度采用R语言完成,复杂网络抗毁性分析采用python完成,此外还涉及一些的数据预处理及分析内容采用python完成。
由于数据量较大,采用百度网盘形式分享(链接:https://pan.baidu.com/s/1UFV6Pq6XPud3QEvjO1qmHw 提取码:soso)
相关参考文献及链接如下:
[1]https://www.zybuluo.com/fanxy/note/1654237
[2]刘莹.基于MES测度的我国金融机构系统性风险研究[J].市场研究,2019(10):32-35.DOI:10.13999/j.cnki.scyj.2019.10.011.
[3]卜林,李政.我国上市金融机构系统性风险溢出研究——基于CoVaR和MES的比较分析[J].当代财经,2015(06):55-65.DOI:10.13676/j.cnki.cn36-1030/f.2015.06.007.
[4]宋宁宁,韩华,吴翎燕.一种基于阈值构建金融网络的新方法[J].计算机工程与应用,2015,51(06):249-253.
[5]任英华,谢佳汇,周金龙,张洁莹.基于复杂网络的商业银行流动性风险评价[J].湖南大学学报(社会科学版),2020,34(04):65-73.DOI:10.16339/j.cnki.hdxbskb.2020.04.009.
[6]党亚茹,丁飞雅,高峰.我国航班流网络抗毁性实证分析[J].交通运输系统工程与信息,2012,12(06):177-185.DOI:10.16097/j.cnki.1009-6744.2012.06.001.
[7]赵静,韩永启,周继彪.蓄意攻击下城市轨道交通网络抗毁性分析[J].城市道桥与防洪,2020(06):203-206+25-26.DOI:10.16799/j.cnki.csdqyfh.2020.06.062.
[8]赵军产,王少薇,陆君安,王敬童.疫情背景下全球股市网络的抗毁性及预警研究[J].复杂系统与复杂性科学,2022,19(01):52-59.DOI:10.13306/j.1672-3813.2022.01.007.
[9]微信公众号:单哥的科研日常

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