用多层线性响应模型对时间序列中的极端波动进行建模和生成
Yusuke Naritomi1, Tetsuya Takaishi2, Takanori Adachi1
1Graduate School of Management, Tokyo Metropolitan University, 18F Marunouchi Eiraku Building, 1-4-1 Marunouchi, Chiyoda-ku, Tokyo 100-0005, Japan.
一个新的多层线性响应模型 (MLRM) 产生具有异常动态的时间序列数据,模仿COVID-19流行病等现实事件. 这种可解释的模型捕捉了金融市场中观察到的沉重的特征和极端波动.
科学领域:
- 物理
- 经济计量学
- 数据科学
背景情况:
- 传统的单层线性响应模型 (SLRM) 在捕捉复杂的动态方面存在局限性.
- 在包括金融在内的各个领域普遍存在极端波动和沉重尾巴的异常动态.
- 需要能够生成现实的异常数据的可解释模型至关重要.
研究的目的:
- 提出一个能够生成具有异常动态的时间序列数据的多层线性响应模型 (MLRM).
- 扩展传统的线性响应理论的能力,包括非线性相互作用.
- 展示MLRM在复制现实世界现象中的适用性,例如COVID-19大流行期间观察到的现象.
主要方法:
- 通过扩展单层线性响应模型 (SLRM) 开发多层线性响应模型 (MLRM).
- 在MLRM框架内引入非线性相互作用.
- 应用MLRM以使用大流行前的财务数据生成时间序列数据.
- 从MLRM生成的数据分析日志回报率和实现的波动性.
主要成果:
- MLRM成功生成了具有异常动态的时间序列数据.
- 从MLRM生成的数据中获得的日志回报和实现的波动性表现出重尾特征,与经验观察一致.
- 该模型展示了极端波动和高波动时期的尾巴行为特征的能力.
结论:
- 拟议的MLRM是生成具有异常动态的可解释时间序列数据的有效工具.
- 在不依赖于机器学习的情况下,MLRM为研究金融等领域的复杂系统提供了有价值的框架.
- 该模型能够捕捉重尾分布和极端事件,从而可以了解金融市场在危机期间的行为.
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