识别金融价格跳跃的新类别与波段
Cecilia Aubrun1,2, Rudy Morel1,3,4, Michael Benzaquen1,2,5
1Chair of Econophysics and Complex Systems, École Polytechnique, Palaiseau Cedex 91128, France.
概括
本研究引入了一种新方法,用于使用波形分析对股票价格跳跃进行分类. 调查结果显示,波动性不对称,局部平均值逆转和趋势是关键特征,而传染导致了许多股票的共同跳跃.
科学领域:
- 量化金融 量化金融
- 时间序列分析时间序列分析
- 计算经济学的计算经济学
背景情况:
- 股票价格的跳跃是金融市场的关键事件.
- 区分外源性 (新闻驱动) 和内源性 (市场驱动) 跳跃对于风险管理至关重要.
- 现有的方法可能无法完全捕捉跳跃行为的复杂动态.
研究的目的:
- 开发一个无监督的股票价格跳跃的分类框架.
- 为了确定区分跳跃类型的关键特征.
- 调查股票价格暴的驱动因素.
主要方法:
- 利用时间序列数据的多尺度波纹表示.
- 应用于股票价格数据的无监督分类.
- 分析了诸如波动不对称性,局部平均值逆转和趋势等特征.
主要成果:
- 证实了波动的时间不对称性作为外源跳跃和内源跳跃之间的主要差异化因素.
- 确定了当地的平均逆转和趋势作为跳跃分类的额外关键特征.
- 发现大量的股票合是由内源性传染机制驱动的.
结论:
- 基于波形的框架有效地对股票价格的跳跃进行分类.
- 使用已识别的特征可以识别新的跳跃类别.
- 内源性传染在库存跳中起着令人惊的重要作用.
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