在财务时间序列中提前预警政权转换:一个异构分类网络模型
Linxi Wang1, Sufang An2,3, Zhiliang Dong3,4
1School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang, China.
PloS one
|October 8, 2025
概括
本研究引入了一种新的方法,用于检测金融时间序列中的政权切换,使用异构分类网络和隐藏的马尔科夫模型. 它有效地识别了市场风险的早期预警信号.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 时间序列分析时间序列分析
背景情况:
- 传统的制度模型忽略了时间序列的非线性和动态不确定性.
- 专注于单点波动限制了对复杂金融系统的理解.
研究的目的:
- 开发一种可靠的方法来检测金融时间序列中的政权切换和预警信号.
- 揭示具有固有的不确定性的非线性时间序列的隐藏结构.
- 加强对金融市场动态的分析.
主要方法:
- 与隐藏的马尔科夫模型 (HMM) 集成一个异种类型的网络.
- 自主回归移动平均值-通用自主回归条件异种性 (ARMA-GARCH) 模型的应用.
- 利用机器学习算法来进行动态过程的表征.
- 网络社区结构分析用于政权检测.
主要成果:
- 在不同时期的标准普尔500指数时间序列中成功检测到关键制度切换.
- 分析了政权演变和早期预警信号,提取了关键特征.
- 证明了在非线性时间序列中发现隐藏结构的能力.
结论:
- 拟议的模型有效地描述动态波动,并检测模式切换.
- 为金融风险预警系统提供了坚实的理论基础.
- 为决策者和市场投资者在管理金融风险方面提供了宝贵的见解.
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