在一个复杂的金融系统中,从溢出网络的动态视角来看,对制度转换的早期预警
Sufang An1,2, Xiangyun Gao3,4, Feng An5
1School of Management, Hebei GEO University, Shijiazhuang 050031, China.
iScience
|April 9, 2025
概括
本研究介绍了金融制度转换的新型预警模型,利用溢出网络和机器学习来检测动态相互依赖. 随机森林模型在识别风险管理关键信号方面表现出卓越的表现.
科学领域:
- 金融计量经济学 金融计量经济学
- 网络分析 网络分析
- 机器学习 机器学习
背景情况:
- 金融风险管理需要及早检测制度的转换.
- 现有的研究往往忽视了跨多个时间序列的动态溢出效应.
- 复杂的金融系统表现出影响市场行为的相互联系.
研究的目的:
- 为金融制度转换开发先进的预警模型.
- 将动态溢出效应纳入一个多变量框架.
- 为了提高市场变化的预测准确度.
主要方法:
- 开发一个溢出网络模型来分析相互依赖.
- 集成用于信号检测的机器学习算法.
- 对能源价格和股票市场指数数据的应用.
主要成果:
- 使用网络指标确定了关键溢出网络.
- 六个机器学习模型被评估为早期预警信号检测.
- 随机森林模型在捕获模式切换信号方面表现最好.
结论:
- 拟议的模型有效地捕获调节切换的早期预警信号.
- 动态溢出效应分析对于理解复杂的金融系统至关重要.
- 这些发现为政策制定者和风险管理投资者提供了宝贵的见解.
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