一个基于神经微分方程和尺度相似性的持续波动预测模型.
IEEE transactions on neural networks and learning systems
|March 27, 2024
概括
本研究介绍了一种使用神经微分方程进行更准确的金融波动性预测的持续波动性预测模型 (CVFM). 在预测准确性和识别高波动性方面,CVFM的表现优于现有模型.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 金融计量经济学 金融计量经济学
背景情况:
- 波动性预测在金融领域至关重要,但由于波动性的持续性,离散时间模型会丢失信息.
- 现有的模型往往无法捕捉金融波动的持续进化行为.
研究的目的:
- 提出一种基于神经网络的新型模型,即持续波动预测模型 (CVFM),以改进波动预测.
- 解决离散时间模型在捕捉连续波动动态方面的局限性.
主要方法:
- 引入了一个由神经微分方程 (NDEs) 控制的连续时间潜伏过程,以建模波动.
- 开发了一个基于尺度相似性的机制,用现实世界的数据校准潜在过程进化,即使没有高频观测.
主要成果:
- 在6个真实世界股票指数数据集上,CVFM表现出卓越的表现.
- 该模型在预测准确性和高波动性识别方面明显优于现有方法.
结论:
- 拟议的持续波动预测模型 (CVFM) 有效地捕捉了波动的持续性质.
- 在金融波动预测准确度和高波动事件检测方面,CVFM提供了显著的进步.
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