在复杂的金融系统中,动态预测和波动同步机制
Jiang-Cheng Li1, Jin Guo2, Rui Ma1
1School of Economics, Yunnan University of Finance and Economics, Kunming, People's Republic of China.
PloS one
|October 31, 2025
概括
这项研究引入了一种用于预测股票市场波动性同步的新方法,这对于理解金融风险传染至关重要. 该模型准确预测同步,超越现有方法,并识别关键风险事件.
科学领域:
- 金融计量经济学 金融计量经济学
- 复杂系统分析 复杂系统分析
- 机器学习应用 机器学习应用
背景情况:
- 波动性同步是金融市场的关键传染机制,有助于系统性风险.
- 现有的模型很难准确地捕捉和预测动态波动同步.
- 了解这些同步模式对于金融稳定至关重要.
研究的目的:
- 开发一种用于金融市场动态波动同步的新型预测方法.
- 用现实世界股票市场数据分析拟议方法的预测性能.
- 为理解复杂的金融系统行为和风险事件提供全面的框架.
主要方法:
- 与波动同步分析框架结合的随机波动模型的构建.
- 整合机器学习技术和一个滚动周期窗口用于动态预测.
- 使用上海复合指数 (SSEC) 和深组件指数 (SZI) 的高频数据进行实证分析,采用多个损失函数和高级预测能力 (SPA) 测试.
主要成果:
- 拟议的模型在样本估计中显示出与市场行为的高度一致性.
- 该方法在预测股票市场波动性同步准确度方面明显优于其他模型.
- 动态模拟和多变量实证分析成功确定了重大风险事件.
结论:
- 开发的方法提供了一个强大的框架,用于预测和理解金融波动的同步.
- 这些发现增强了我们管理系统性金融风险和预防危机的能力.
- 这种方法为复杂的金融系统的动态提供了有价值的见解.
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